Advanced Statistical Analysis - Science topic

Hello,

You can use a mixed-design ANOVA with language ability as a between-subjects factor and the avatar's language ability as a within-subjects factor for the bilingual group only. Planned contrasts or post-hoc tests can compare the control group to bilinguals.

Hope this helps

IYH Dear Adam Clifton

Based on your description, I suggest applying Generalized Linear Mixed Models (GLMM) followed by post hoc pairwise comparisons with Bonferroni adjustment. This approach allows you to incorporate multiple responses from the same participant, handle categorical variables, and determine statistically significant differences between conditions.

Example how you could structure the GLMM:

Once fitted, execute post hoc pairwise comparisons between HRTF types to investigate significant preferences. As suggested, I would adjust p-values accordingly using Bonferroni corrections to maintain strict control over family-wise error rates.

Additionally, calculate the proportion of consistent selections made by each participant for each condition, excluding those who consistently chose the same option regardless of presentation. Then run chi-square goodness-of-fit tests comparing expected frequencies (equal distribution) versus observed distributions derived from participants' consistent preferences.

Kindly be aware (or your prof may tell you :D) that your current sample size is relatively small, drawing definitive conclusions is still possible after properly accounting for dependencies and handling multiple comparisons.

Maybe a word on Bonferroni: The so-called Bonferroni adjustment serves to counteract the issue of multiple comparisons, reducing the chance of falsely rejecting null hypotheses. Essentially, it establishes stricter criteria for concluding statistical significance, thereby maintaining appropriate error rates in the presence of multiple tests.

Why is this needed? When executing post hoc pairwise comparisons after fitting a GLMM, running multiple tests simultaneously, increases the risk of encountering false positives. To mitigate this risk, we apply a corrected alpha level determined by dividing the desired family-wise error rate (Type I error rate applicable to the entire set of comparisons) by the number of tests performed.

Typically, you would choose α=0.05 or α=0.01 as baseline significance threshold. In cases w multiple comparisons, we set α_bonferroni=(α/number of tests). For example, suppose you intend to compare six means with a baseline α=0.05. In that situation, you'd compute α_bonferroni=0.05/6≈0.0083, meaning that you now require stronger evidence (smaller p-value) to claim significance.

Bonferroni is a safe conservative choice (however sometimes too much so, especially when tests exhibit low dependence) However, it guarantees strict control of the family-wise error rate. Other alternatives are Šídák or Holm–Šídák: Compared to Bonferroni, both Šídák and Holm–Šídák demonstrate reduced conservativeness ie lesser reduction in statistical power. In practical applications, choosing between Šídák and Holm–Šídák depends on the degree of dependence amongst the tests, the cost of committing type I or type II errors, and the complexity of computations tolerated.

It is possible to use the same variable twice, once as a mediator and once as an independent variable. This methodology enables a more comprehensive examination of the connections inside the model.

For Reference:

https://www.statalist.org/forums/forum/general-stata-discussion/general/1350511-testing-mediation-between-10-independent-variables-and-1-dv-with-mixed

Check this link.

The pooled standard deviation is a method for estimating a single standard deviation to represent all independent samples or groups in your study when they are assumed to come from populations with a common standard deviation. The pooled standard deviation is the average spread of all data points about their group mean (not the overall mean). It is a weighted average of each group's standard deviation.

Attached is the formula.

In my opinion, Normalizing each component will solve problem!

Hi! Was your query answered? I am confused about a similar set up of mine!

P.S.: Plaul Bliese has a multilevel tutorial for R, where he shows how to calcualte the above mentioned indices, as well as others, since all have their specific problems, which would lead too far to discuss them here.

Looking further - your package may be reporting an uncentred VIF in place of or in addition to a centred VIF. There is an apparently unresolved debate in the literature about when or why that's useful. For practical purpose in most regressions it seems likely that high uncentred VIF may not be problematic. I've never seen uncentred VIF used in a published paper ...

You need to save the output in an object. In the case of lm(), the best is to save the results in a list. This is achieved automatically if you'd use lapply() instead of for().

models <- lapply(i, function(x) lm(x~d))

Dear Jie Zhang

You're welcome. Wish you the best.

Regards.

Hello Haque Mobassir Imtiyazul Haque Shaikh ,

I agree with your contention that some ideas initially strike most people as 'crazy' both in technical and nontechnical fields. Examples from nontechnical fields include: opposing slavery, gun control, democracy, women voting, environmentalism, climate change, etc. Examples from technical fields include: mRNA vaccines (COVID-19 vaccines from Moderna and Pfizer), prions (self replicating proteins), continental drift, quasicrystalls, Josephson junctions (SQUIDs), quantum mechanics, the personal computer, the Internet, the airplane, radio, TV, electricity, etc. One person's 'crazy' idea may eventually become widely accepted, and even commercially important. And don't forget, many 'crazy' ideas originated from by-the-book investigations: the idea of the quantum of energy arose from Max Planck's tireless attempts at trying to explain the shape of the blackbody curve using classical thermodynamics, and superconductivity in some metals was the result of a rather pedestrian checking of electrical conductivity of metals at liquid helium temperatures - no one expected superconductivity and no theory predicted it.

I really like your question.

Regards,

Tom Cuff

There should be a causal effect of the independent variables on the dependent variable in regression analysis. Primary data gathered through questionnaire for the dependent variable would be influenced by the current happenings while the independent variables based on secondary data was influenced by past or historical happenings. Therefore, there would not true linkages between independent variables and the dependent variable. Therefore, running a regression with both Secondary and primary data in the same model would not give you best outcome.

I think you can try something like this

Hi,

I wonder why you would like to ignore the abundance information?

Based on a species-site abundance matrix, you could calculate a dissimilarty matrix (if the abundance data should be considered, I would use bray-curtis similarity) and conduct a mantel test between all three dissimilarity matrices to test for correlations between the species compositions of the three sites.

In addition to the loss of power that Kelvyn Jones mentioned, when you carve a quantitative variable into categories, the fitted values are forced to follow an artificial step function. The attached image shows the relationship between age (X) and total cholesterol (Y). Notice that when X is carved into 3 categories, the fitted values are forced to follow a step function. Notice that people near a cut-point who have tiny differences in age have quite large differences in fitted values. And notice that people who fall at opposite ends of an age category have the same fitted value, despite having fairly large age differences. With that in mind, the fitted values from the linear regression model make a lot more sense, I think! HTH.

Sundas Azim, In your study with three independent variables (IVs) and one dependent variable (DV), it's generally not recommended to convert a continuous IV like PI into a categorical variable, as doing so can lead to a loss of information and statistical power. Instead, you can employ hierarchical regression analysis or multiple regression analysis with interaction terms to examine the interactive effects of all three IVs on the DV. To investigate your hypothesis that COO will be highest for the combination of High POP, high SOE, and high PI, you can create interaction terms for these conditions and include them in your regression model. For instance, you can create a variable that multiplies the values of POP, SOE, and PI when they are all high. Similarly, you can create interaction terms for other combinations you want to explore. This approach allows you to assess the impact of each IV while considering their interactive effects, avoiding the need to categorize the continuous variable PI. In your data sheet, you would enter the actual continuous values for POP and SOE, and for PI, you would enter the measured values. Ensure you center your continuous IVs (subtract the mean from each score) to aid in the interpretation of interaction effects. This approach will provide a more robust and informative analysis for your research question.

Thank you for your question. I have searched the web for information about the Diagonally Weighted Least Squares (DWLS) and Unweighted Least Squares (ULS) estimators, and I have found some relevant sources that may help you with your decision.

One of the factors that you should consider when choosing between DWLS and ULS is the sample size. According to Forero et al. (2009)1, DWLS tends to perform better than ULS when the sample size is small (less than 200), but ULS tends to perform better than DWLS when the sample size is large (more than 1000). Since your sample size is 658, it falls in the intermediate range, where both methods may provide similar results.

Another factor that you should consider is the degree of non-normality of your data. According to Finney and DiStefano (2006), DWLS is more robust to non-normality than ULS, especially when the data are highly skewed or kurtotic. However, ULS may be more efficient than DWLS when the data are moderately non-normal or close to normal. Since your data have slight negative skewness and kurtosis, it may not be a serious violation of the ULS assumptions.

A third factor that you should consider is the model fit and parameter estimates. According to Forero et al. (2009)1, both methods provide accurate and similar results overall, but ULS tends to provide more accurate and less variable parameter estimates, as well as more precise standard errors and better coverage rates. However, DWLS has higher convergence rates than ULS, which means that it is less likely to encounter numerical problems or estimation errors.

Based on these factors, it seems that both DWLS and ULS are reasonable choices for your data and model, but ULS may have some advantages over DWLS in terms of efficiency and accuracy. However, you should also check the sensitivity of your results to different estimation methods, and compare them with other criteria such as theoretical plausibility, parsimony, and interpretability.

I hope this answer helps you with your analysis. If you need more information, you can refer to the sources that I have cited below.

1: Factor analysis with ordinal indicators: A Monte Carlo study comparing DWLS and ULS estimation by Carlos G. Forero, Alberto Maydeu-Olivares & David Gallardo-Pujol in British Journal of Mathematical and Statistical Psychology (2009)

: Non-normal and categorical data in structural equation modeling by Sara J. Finney & Christine DiStefano in Structural equation modeling: A second course (2006)

Good luck

That makes sense. Are you comparing the cross-lagged panel (auto)regression (path) coefficients to zero-order correlations? This could be part of the issue (explain the "discrepancy"/low autoregressive stability coefficient). Regression coefficients are not equal to zero-order (bivariate) correlations. The regression coefficients take the correlation with other independent variables into account. This may explain why the autoregressive "stability" coefficients in your model look very different from the zero-order correlations. It is impossible to know without looking at your data and model in more detail.

The model fit does not look completely horrible at first sight but the chi-square test is significant and the RMSEA value is a bit high. I would take a look at model residuals and/or modification indices to find out where the model may be misspecified.

The significance level of a test is a predetermined threshold that is used to determine if the evidence from the sample provides enough support to reject the null hypothesis. It is typically denoted by the symbol α. The significance level is relevant in several ways: Type I error rate, Confidence level, Decision-makingm, Sample size determination

In summary, the significance level guides the researcher in interpreting the test results and making informed decisions regarding the null and alternative hypotheses.

There are seven numbers and you stated that 10000 appears twice, which means that there are eight numbers. In calculation of average denominator is 7 which means that 10000 cannot apper twice. Range is calculated as 23000 - 10000 instead as 54830 - 5603 = 49227. Sum of those numbers is 128573, not 103573. Are you sure about those numbers ?

The sequential Mann-Kendall test, also known as the Mann-Kendall-Sneyers (MKS) test, is a variation of the Mann-Kendall test that aims to detect trends in time series data. The test involves comparing the original time series to its reverse version to identify potential trends. The graphical representation of the direct series (u(t)) and the backward series (u'(t)) can provide insights into the presence or absence of trends. However, the interpretation can be nuanced.

Dufek (2008) suggests that if there is no trend, the curves of the direct and backward series will overlap several times. In your case, you observe two to three overlaps, often with sequences that exhibit significant trends. This situation requires careful consideration:

In conclusion, while the observation of overlaps in the graphical representation of the sequential Mann-Kendall test might suggest a lack of clear trends, the presence of significant trends in some segments complicates the interpretation. It's important to analyze the trends, magnitudes, and durations of both overlaps and significant trends while considering the broader context of your data and the subject matter you're studying. If possible, consulting with a statistician or subject-matter expert might help you make a more informed interpretation of your findings.

Discriminant function technique involves the development of selection criteria on a combination of various characters and aids the breeder in indirect selection for genetic improvement in yield. In plant breeding, the selection index refers to a linear combination of characters associated with yield.

R in Power BI ?

It should not contain complex R syntaxes though.

https://learn.microsoft.com/en-us/power-bi/create-reports/desktop-r-visuals

Hi Carlos Jimenez-Gallardo ,

precisely: two sides of the same coin.

Hello Pavel Makovsky. In that case, modify the code I posted earlier. With your dataset open and active:

REGRESSION

/STATISTICS COEFF OUTS CI(95) R ANOVA

/DEPENDENT YourDV

/METHOD=ENTER your explanatory variables

/SAVE PRED(yhat1).

* Now use another set of coefficients to generate yhat2.

COMPUTE yhat2 = use the other regression coefficients here but with the variables in your dataset.

REGRESSION

/STATISTICS R

/DEPENDENT YourDV

/METHOD=ENTER yhat1.

REGRESSION

/STATISTICS R

/DEPENDENT YourDV

/METHOD=ENTER yhat2.

To specify the significance of the marginal effects in binary logistic regression analysis, you can interpret the results by examining the p-values, B (coefficient estimates), and Exp(B) (exponentiated coefficient estimates) values. The p-value indicates the statistical significance of each predictor variable's effect on the outcome variable. A low p-value (typically less than 0.05) suggests a significant effect. The B values represent the estimated change in the log-odds of the outcome for a one-unit change in the predictor, with positive values indicating a positive association and negative values indicating a negative association. Exp(B) provides the odds ratio, which quantifies the change in odds for a one-unit increase in the predictor. An Exp(B) greater than 1 indicates an increased odds of the outcome, while a value less than 1 implies a decreased odds. By considering the significance of the marginal effects, you can determine the direction, magnitude, and statistical significance of the predictor variables' impacts on the binary outcome variable in your logistic regression analysis.

First, to change the reference level You can specify the order of items in categorical array

categorical(A,[1, 2, 3],{'red', 'green', 'blue'}) or

categorical(A,[3, 2, 1],{'blue', 'green', 'red'})

Second, You can specify the correct hypothesis matrix for coefTest function for comparison between every pair of categories.

Yes, it is possible to estimate the pre-post correlation coefficient in a meta-analysis using various methods, such as imputing a value or using a range of plausible values. Here are a few options:

Once you have estimated the pre-post correlation coefficient, you can enter it into the appropriate field in the CMA software and proceed with the analysis. It is important to carefully consider the implications of the chosen correlation coefficient and to conduct sensitivity analyses to test the robustness of the results.

Hello Ravisha,

If cases are randomly assigned to treatment condition, there's no reason that post-only design results should be considered uninformative.

Designs with pre-post measures can offer the added benefits of: (a) allowing for estimation of change (though unless scores are completely reliable, the change scores will be less reliable than either the pre- or post- score by itself); or (b) pre-scores can be used as a covariate, to adjust for randomly occurring differences across groups.

One noted threat to pre-post designs is that if the interval separating them is too short, the post-results, and therefore group comparisons, can be biased, especially with measures of affect.

Ultimately, the answer depends on what your target ES might be: If it is post-treatment differences across groups/conditions, then either design can contribute. You could estimate ES separately by study type to see whether inclusion of pre-test appears to account for differences.

If it is strictly pre-post change, then post-only designs can't contribute (again, though, note the caveats above).

Good luck with your work.

At the same time，Collect two variables data,As a sample,After collecting N samples over time,erform data regression analysis on them,The correlation coefficient will be obtained.

In attachment an R script with the BH post-hoc test based on Benjamini & Hochberg (1995). You could replace this with Bonferroni, but in my opinion this last method is too conservative.

Why would these variables have to be normal? As far as I understand our problem, a logistic model might do well. You can try it with my software "FittingKVdm", but if you can send me some dat, I can try it for you.

Very helpful paper with references:

Best,

Wadie

Yes, that would be correct.

As your outcome/ dependent measure is only at one time point you would not have to consider time in relation to the outcome, so not a longitudinal model (no variation over time to model).

That is not to say that time may or may not be important in your research question. If trends/ differences/ averages in the repeated measures of independent variables are important in relation to your outcome then you can find ways to incorporate these things into your modelling strategy (in the way that you choose to use your repeated independent measures - being guided by research questions).

You can use layout() to define a matrix of plots with different heights/widths. In your case, this will produce a layout similar to your picture:

m <- rbind(c(0,1,0), c(2:4))

layout(m, widths = c(1,1,1.5), heights = c(1,1))

par(oma = c(3,3,3,3), mar = c(0,0,0,0), las = 1, xaxs = "i", yaxs="i")

plot(NA, xlim = c(-1, 9), ylim = c(-1, 4), xaxt="n", yaxt="n")

axis(2, at = 0:4)

axis(3, at = 0:4 * 2)

plot(NA, xlim = c(0, 4), ylim = c(0, 3.5), xaxt="n", yaxt="n")

axis(1, at = 0:4)

axis(2)

plot(NA, xlim = c(-1, 9), ylim = c(0, 3.5), xaxt="n", yaxt="n")

plot(NA, xlim = c(0, 7), ylim = c(0, 3.5), xaxt="n", yaxt="n")

axis(1, at = 0:7)

axis(4)

Good morning, without the code it is difficilt to know where is the difference I do not use Python i work on R but maybe these difference is due to the stage of spitting dataset do you try to add thr same number in the count of generator of randomly for example seed(1234) (if my memory is good this function is also used in Python language. Were your results and metrics of evaluation totally different? In this case, mayve there is a reliability issue in your model. You should check your data preparation and features selection .

I was unsure how to pool SD from the SE without knowing N. A method I found used the "baseline SD" for each group.

It depends on the cost function and the model that you are using. Gradient descent will converge to the optimal value (or very close to it) of the training loss function, given a properly set learning rate, if the optimization problem is convex with respect to the parameters. That is the case for linear regression using the mean squared error loss, or logistic regression using cross entropy. For the case of neural networks with several layers and non-linearities none of these loss functions make the problem convex, therefore there is no guarantee that you will find the optimal value. The same would happen if you used logistic regression with the mean squared error instead of cross entropy.

An important thing to note is that when I talk about the optimal value, I mean the value that minimizes the loss in your training set. It is always possible to overfit, which means that you find the optimal parameters for your training set, but those parameters make inaccurate predictions on the test set.

Dear Dr. Gaško,

I'm glad to hear that. You are very welcome.

Best wishes.

Not sure I understand your question. If there is a single predictor and by regression you mean linear OLS regression, then the r is the same. Can you provide more details>

Using R code, the bootstrap method can estimate the error rate in linear discriminant analysis. First, the data must be split into a training set and a test set and then normalized. The lda() function can then be used to run the calculations twice, with CV=TRUE for the first run to get predictions of class membership derived from leave-one-out cross-validation. The second run should use CV=FALSE to get predictions of class membership based on the entire training set. The true error rate estimator BT2 of the restricted linear or quadratic discriminant analysis can be calculated using the dawai package in R. Finally, resampling methods such as bootstrapping can be used to estimate the test error rate.

I've only glanced quickly at those two resources, but are you sure they are addressing the same thing? Yates' (continuity) correction as typically described entails subtraction 0.5 from |O-E| before squaring in the usual equation for Pearson's Chi2. E.g.,

But adding 0.5 to each cell in a 2x2 table is generally done to avoid division by 0 (e.g., when computing an odds ratio), not to correct for continuity (AFAIK). This is what makes me wonder if your two resources are really addressing the same issues. But as I said, I only had time for a very quick glance at each. HTH.

In order for the benefit to prevail, I have verified a group of packages that do the add of The robust confidence ellipses of 97.5%

View them here by package and its function

1- ellipses () using the package 'ellipse'

## ellipses () using the package 'rrcov'

## ellipses () using the package 'cluster'

Try to publish on your own then you have complete control. Collaborators will steal your data and treat you badly :)

You can use standard procedures for the fixed effects estimates as they are akin to regression model estimates if the response is continuous. Things are more complicated of the response is categorical.

A scale reliability of .39 (and even .53!) is very low. Even if your main focus is not on the psychometric properties of your measures, you should still care about those properties. Inadequate reliability and validity can jeopardize your substantive results.

My recommendation would be to examine why you get such a low alpha value. Most importantly, you should first check whether each scale (item set) can be seen as unidimensional (measuring a single factor). This is usually done by running a confirmatory factor analysis (CFA) or item response theory analysis. Unidimensionality is a prerequisite for a meaningful interpretation of Cronbach's alpha (alpha is a composite reliability index for essentially tau-equivalent measures). CFA allows you to test the assumption of unidimensionality/essential tau equivalence and to examine the item loadings.

Also, you can take a look at the item intercorrelations. If some items have low correlations with others, this may indicate that they do not measure the same factor (and/or that they contain a lot of measurement error). Another reason for a low alpha value can be an insufficient number of items.

Dear Panagiotis N. Stoikos

If your data is not normally distributed, you should use non-parametric statistical tests such as Wilcoxon rank sum tests or Mann-Whitney U tests in order to compare the expression levels between the two groups.

Regarding the one tailed or two tailed. one tailed can specify the direction of the effect (positive or negative) but the two tailed one can be used for both direction at the same time.

Best...

In general, permanova is a test of the effect of two parallel variables on the organism. It is equivalent to two one-way ANOVAs. Whereas GLMS is equivalent to the combined effect of all factors, in GLMS you can derive the contribution of each variable to determine the magnitude of the contribution of each environmental factor. You can understand that permanova is the parallel effect of several factors, while GLMS is the combined effect. GLMS is simple and easy to operate in R language.

In the last few months, a colleague of mine has written a version of the PCO-program in R. The first impressions are good, but we need a few more months to test it and prepare a publication aboiut it.

Massimo Sivo very nice of you that you want to try to help your colleague, however, as was mentioned earlier, you should understand experimental design very well before you have sufficient information to construct an appropriate statistical model (that can than provide meaningful insights). To understand experimental design of a clinical trial it is not enough to understand some statistics, you should also understand some medicine. E.g. From what Shahnawaz Ahmad Wani described one can by no means understand what was actually measured, how, and why. It is super challenging to sample 5 patients with appropriate controls to make any kind of biologically meaningful inference about the population of patients. Tons of confounders and no power to take them into account. From how the problem was presented I can image such (confounder) data was not collected. Using such data and shoving them into e.g. mixed model to account for repeated measures (and dependence of some parameters) will most likely give meaningless results. Even if everything was done right but you did not account for a biologically meaningful confounder... it would still be meaningless. For example, Shahnawaz Ahmad Wani told us he is measuring some kind of biochemical parameter from the blood from female patients. Many parameters in the blood change with the part of the menstrual cycle, and it is not clear whether the authors analysed this. Without accounting for such a serious confounder the study makes no sense. Furthermore, how would you construct a model around the dependence of venous blood and "tissue blood". The variables are most definitely related, but we cannot imagine the nature of their dependence without having much more information. So, before asking for data, you should really ask for the explanation of the design to make sure all the confounders were ruled out the the level of design as this is the only scenario in which several lines of code or several clicks in some statistical software will help you in any way to make some kind of inference.

Please be mindful about this kind of stuff, uncertain and misleading conclusions can be very dangerous in medicine and it is healthier for the community to talk about them than to just generate some numbers from the data.

Best,

jan

I'm not sure I fully understand your question but when you have a column with a constant value (e.g., 1), this constant by definition cannot covary with another column/variable. A constant does not have any variance and therefore, the covariance/correlation with another variable will also be zero by definition.

* Create a toy dataset to illustrate.

NEW FILE.

DATASET CLOSE ALL.

DATA LIST LIST / A B C D E (5F1).

BEGIN DATA

1 0 0 0 0

0 1 0 0 0

0 0 1 0 0

0 0 0 1 0

0 0 0 0 1

1 1 0 0 0

0 1 1 0 0

0 0 1 1 0

0 0 0 1 1

1 0 2 0 0

END DATA.

IF A EQ 1 and MIN(B,C,D,E) EQ 0 AND MAX(B,C,D,E) EQ 0 severity = 1.

IF B EQ 1 and MIN(A,C,D,E) EQ 0 AND MAX(A,C,D,E) EQ 0 severity = 2.

IF C EQ 1 and MIN(B,A,D,E) EQ 0 AND MAX(B,A,D,E) EQ 0 severity = 3.

IF D EQ 1 and MIN(B,C,A,E) EQ 0 AND MAX(B,C,A,E) EQ 0 severity = 4.

IF E EQ 1 and MIN(B,C,D,A) EQ 0 AND MAX(B,C,D,A) EQ 0 severity = 5.

FORMATS severity (F1).

LIST.

* End of code.

Q. Is it possible for any of the variables A to E to be missing? If so, what do you want to do in that case?

Maybe the attached is what you mean. David Booth

Ange, I think the easiest way for you to find an answer to your question would be to google something such as "SPSS recode variables YouTube". You'll probably find several sites that demonstrate what you want to do.

All the best with your research.

What do you mean "hypothetical study?" Is this a homework question?

You use beta diversity (β), which is a measure of the difference in composition of species between locations :)

Sorry, Jianhing. But I think you have misunderstood something in the lecture. Frequentist statistics, which is an interpretation of probability to be assigned on the basis of many random experiments.

In this setting, on designs functions of the data (also called statistics) which estimate certain quantities from data. For example, the probability p of a coin to land heads is given from n independent trials with the same coin and just counting the fraction of heads. This is then an estimator for the parameter p.

Each estimator should have desirable properties, as unbiasedness, consistency, efficiency and low variance and so on. Not every estimator has these properties. But, in principle one can proof, whether a given estimator has these properties.

So, it is not a characteristics of frequentist statistics, but a property of an individual estimator based on frequentist statistics.

Thank you so much, Daniel Wright , Jochen Wilhelm , Richard Johansen ! Your answers were very helpful. I was able to do it through the string package.

What if the p-value of the HL test doesn't appear? it just appeared as this code ".". what is that mean? thank you

See also duplicate question: https://www.researchgate.net/post/After_checking_normality_between_two_group_One_had_normal_distribution_and_other_not_What_test_should_be_used_for_comparing_their_mean

Javier Ernesto Vilaso Cadre , tests do not corroborate that a distribution is normal. They may only fail to corroborate that a distribution is not normal, and that may simple be due to the sample size. Actually, such tests only tell you if your sample size is already large enough to see that the normal distribution model (an idealized model!) does not account for all features of a real distribution. So actually they don't give you any useful information (you may fail to see relevant discrepancies because the sample size is too small, or you may get blinede with "statistically significant" discrepancies that are irrelevant for your problem). The only sensible way is to understand the variable and have some theoretical justification of its distribution, and then to judge if the presumed discrepancies are relevent for your problem. One may then certainly have a look at the empirical distribution of the observed data: if it screams at you that your thoughts and arguments are very likely very wrong you may go back and refine or deepen your understanding of the data-gereative process you like to study.

Hi Mairtin Mac Siurtain, thanks so much for your reply!

This is great information. I'll take a look first at MANOVA and then at RBSCIs if indicated. Many thanks!

Best,

Matt

نعم يؤدي الى اختلاف النتائج لان كل اداة احصائية تستخدم وفق معايير خاصة لها لايمكن استخدام عدة وسائل احصائية للغرض نفسه David Morse

You should try this text.

Harrer, M., Cuijpers, P., Furukawa, T.A., & Ebert, D.D. (2021). Doing Meta-Analysis with R: A Hands-On Guide. Boca Raton, FL and London: Chapman & Hall/CRC Press. ISBN 978-0-367-61007-4.

I found the code linked to by Jeff Rothschild to be a little difficult to follow. I adapted the code to make it a little more straightforward. Below is some R code which calculates the confidence interval by this method and by bootstrap. RMSE is calculated from a vector of Actual values and a vector of Predicted values.

I have no first-hand experience to offer you either. But this systematic review article may give you some ideas.

I suggest using mosaic plots rather than (stacked) barplots to visualize your data.

The chi²- and p-values can be calculated simply via chi²-tests (one for each ABA conc) -- assuming the data are all independent (again, please note that seedlings on the same plate are not independent). If you have no possibility to account for this (using a hierarchical/multilevel/mixed model), you may ignore this in the analysis but then interpret the results more carefully (e.g., use a more stringent level of significance than usual).

A binomial model (including genotype and ABA conc as well as their interaction) would allow you to analyse the difference between genotypes in conjunction with ABA conc. However, due to the given experimental design (only three different conc values) this is cumbersome to interpret (because you cannot establish a meaningful functional relationship between cons and probability of germination).

Kindly follow the SPSS structure for determining the critical value here: It is pretty simple and intuitive

You should use the model that makes more sense, practically and/or theoretically. A high R² is not in indication for the "goodness" of the model. A higher R² can also mean that the model makes more wrong predictions with a higher precision.

Do not build your model based on observed data. Build your model based on understanding (theory) and the targeted purpose (simple prediction, exptrapolation (e.g. forecast), testing meaningful hypotheses etc.)

Removing a variable from the model changes the meaning of the intercept. The intercepts in the two models have different meanings. They are (very usually) not comparable. The hypothesis tests of the intercepts of the two models test very different hypotheses.

PS: a "non-significant" intercept term just means that the data are not sufficient to statistically distinguish the estimated value (the log odds given all X=0) from 0, what means that you cannot distinguish the probability of the event (given all X=0) from 0.5 (the data are compatible with probabilities larger and lower 0.5). This is rarely a sensible hypothesis to test.

Hi dear Mario

I'm trying to classify hard-wired human settlement preferences based on archaeological evidence in the Paleolithic era, this is an effort to update biophilia hypothesis.

But for doing hypothesis testing i need to quantify the data and the first step is to make a database from as many as possible Paleolithic geolocations then put them through GIS analysis and test my hypothesis based on the data acquired in SEM (which mostly is that what environmental attribute has how much impact on our decision to select a settlement in what period of paleohistory).

PS. I tested a small database but i was wondering if there is a geolocation database of archaeological sites that i coulden't find yet.

Bests

SAM

I think you have answered your question: "It should be noted that when I plot the data, very very few participants chose yes in response to the neutral and positive images. Almost all yes responses were given in response to the negative images."

This is what you'd expect even in a simple 2x2 design. If the probability of a yes response in the positive condition is very high and the probability very low in the negative condition then the OR could be high as its the ratio of a big probability to a very low one.

This isn't unnatural unless the raw probabilities don't reflect this pattern. (There might still be issues but not from what you described).