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Usually, you can select the structure and paste in MSword, You can also go to save as, and save as an image. If it does not work, then It has to to do with having the right license: informatics-support.perkinelmer.com/hc/en-us/articles/4408239519892-Copy-and-paste-from-ChemDraw-trial-to-Microsoft-Word-is-not-working-

That's a great question, and it's not dumb at all! Assessing the normality of a population when you don't have access to the entire population data can be challenging. However, there are some techniques you can use to gain insights into the normality of your sample. Here are a few approaches: Graphical methods: You can create visualizations, such as histograms or box plots, to examine the shape of your sample data. If the data roughly follows a bell-shaped curve, it suggests normality. However, keep in mind that visual inspection alone is not conclusive evidence of normality. Quantile-quantile (Q-Q) plots: Q-Q plots compare the distribution of your sample data to the expected distribution of a normal population. If the points on the plot closely follow a straight line, it indicates that your data aligns with a normal distribution. Various statistical software packages can generate Q-Q plots for you. Statistical tests: There are several statistical tests available to assess normality, such as the Shapiro-Wilk test, Anderson-Darling test, or Kolmogorov-Smirnov test. These tests evaluate the null hypothesis that your sample comes from a normally distributed population. However, it's important to note that these tests are sensitive to sample size, and small departures from normality can lead to rejecting the null hypothesis. Therefore, it's recommended to interpret the results cautiously and consider other factors as well. Central Limit Theorem (CLT): In many cases, even if your population is not normally distributed, the sample means tend to follow a normal distribution due to the Central Limit Theorem. This theorem states that as the sample size increases, the sampling distribution of the mean approaches a normal distribution, regardless of the shape of the population distribution. Therefore, if your sample size is sufficiently large (usually around 30 or more), you can assume that the sample mean is approximately normally distributed.

@@learnersj Thank you. I would go with the first method plus the Shapiro-Wilk test just so I have a fair idea of the normality of my data. My decision is based on the resources at my disposal. I have SigmaPlot and I guess the t-test report includes a Shapiro-Wilk result which tells you whether your selected datasets passed normality or not. However, one has to be careful with the sample size involved in interpreting the report or results. I also think one key thing to be considered during statistical analysis in SigmaPlot is how you arrange your data. In my case I am interested in testing if there is a significant difference between the 4 different concentrations (treat groups) at two given temperature conditions (room temperature or 28oC) during spontaneous tail coiling testing. Given the above information it means I can’t use t-test right? Because I have more than 2 datasets. However, I can use one way ANOVA. Now how do I know which of the groups gave a significant response between the given temperature conditions? Thank you.

@@akpalumoseskudjo4981 It's great that you're planning to use a combination of methods, such as graphical analysis and the Shapiro-Wilk test, to assess the normality of your data. That will provide you with a fair idea of the normality assumption. Regarding your analysis with SigmaPlot, it's important to be aware of the features available in the software for statistical analysis. The Shapiro-Wilk test result included in the t-test report can be useful for checking normality. Just remember to consider the sample size when interpreting the result, as smaller sample sizes may lead to a higher likelihood of detecting departures from normality, even if they are not practically significant. In your case, since you have four different concentrations (treatment groups) and two temperature conditions (room temperature and 28°C), you are correct that you cannot use a t-test, as it is suitable for comparing two groups only. Instead, you can employ a one-way ANOVA, which is appropriate for comparing means across three or more groups. To determine which groups showed a significant response between the given temperature conditions, you can use post-hoc tests. Post-hoc tests allow for multiple pairwise comparisons after finding a significant result in the ANOVA. Common post-hoc tests include Tukey's Honestly Significant Difference (HSD), Bonferroni, or Scheffe's method. These tests help identify specific group differences while controlling for the overall experiment-wise error rate. SigmaPlot should have options to perform post-hoc tests following an ANOVA. By conducting the post-hoc tests, you will be able to pinpoint the specific combinations of concentrations and temperature conditions that exhibit statistically significant differences. Remember to consider the assumptions of the one-way ANOVA, such as the assumption of homogeneity of variances and the normality assumption for each group. If there are significant departures from these assumptions, there are alternative non-parametric tests available, such as the Kruskal-Wallis test. Overall, using a one-way ANOVA followed by post-hoc tests will enable you to assess the significance of differences between groups and determine which specific combinations show significant responses between temperature conditions.

@@learnersj Thank you very much. By the ways are you a statistician?

Best bet is to plot a box plot. Outliers will be shown above or below.

chemmazing.perkinelmerinformatics.com/conversations/tipstricks/how-to-adjust-the-length-a-cc-bonds/62cd78d1e6511d675eb8d5d9 Let me know if this helped

Unfortunately one at a time

Yes

Yes it is

Thank you. Appreciate the support.

Hi Michael, I have not faced this issue personally but here is a link to help support.alfasoft.com/hc/en-us/articles/360001138937-Error-Cannot-locate-Export-Filters-section-in-ini-file Let me know if this was helpful

@@learnersj I will let you know thanks for the link.

Hi Nikhil. Thank you! I will keep that in mind. But in short I use Movavi video editor to compile and edit everything. There are other capable software out there too. I easily annotate on the video, add images and edit all in one software.

Send me your email

Yes, it can! Just type write ne a personal statement, and it will. The more information you type in the more refined the personal statement will be

Can I please have your email address.

Thanks for the feedback

Glad it was helpful

As far as I know, SigmaPlot does not have that capability for now

Glad it was helpful

Glad it helped!

Thanks and welcome

Hi Birara, I do not think there is anyway SigmaPlot can add % automatically to your bar You can rather, change your columns to percentages first and then plot them you can also plot your data and then type in the percentages yourself I hope this helps

Thanks Blessing! Just as we discussed to help others who have questions 1. Go to Analysis tab 2. Select Regression Wizard 3. Select New 4. Under equation type your eqn. f=(y*x)+c 5. Go to next line and type fit f to y Under equation everything should look like this; f=(y*x)+c fit f to y 6. Go to Variables and type; x=col(1) y=col(2) Specify which columns have your data 7. Go to Initial Parameters and type some initial estimates for instance c=1 8. You can also add some constraints 9. Select Add As and save your code 10. To access your equation, go to Equation Category and user defined. 11. You can always edit code as well

@@learnersj Thank you so much. You really assisted me more than you mentioned here .🙏

Glad it helped

Thanks