VBA Tips: Build an Excel Add-In About Add-Ins An Excel Add-In is a file (usually with an .xla or .xll extension) that Excel can load when it starts up. The file contains code (VBA in the case of an .xla Add-In) that adds additional functionality to Excel, usually in the form of new functions. Add-Ins provide an excellent way of increasing the power of Excel and they are the ideal vehicle for distributing your custom functions. This article shows you how to write a custom function using Excel VBA and how to save and install it as an Add-In. In the Excel tutorial Working Out a Person's Age - An Introduction to Nested IF Statements I showed how to use IF statements to calculate someone's age from their date of birth. If you are already comfortable with writing custom functions, you can go straight to the section explaining how to save your UDFs as an Add-In. Write the Function An Add-In can contain as many UDFs as you want, and you can add more later simply by opening and editing the Add-In file. Step 2: Enter the Code
The Order of Operations: PEMDAS Purplemath If you are asked to simplify something like "4 + 2×3", the question that naturally arises is "Which way do I do this? Because there are two options!" ...or I could multiply first: Which answer is the right one? MathHelp.com It seems as though the answer depends on which way you look at the problem. To eliminate this confusion, we have some rules of precedence, established at least as far back as the 1500s, called the "order of operations". A common technique for remembering the order of operations is the abbreviation (or, more properly, the "acronym") "PEMDAS", which is turned into the mnemonic phrase "Please Excuse My Dear Aunt Sally". Parentheses (simplify inside 'em) Exponents Multiplication and Division (from left to right) Addition and Subtraction (from left to right) When you have a bunch of operations of the same rank, you just operate from left to right. Content Continues Below Simplify 4 + 32. I need to simplify the term with the exponent before trying to add in the 4:
History of Normal Distribution History of the Normal Distribution Author(s) David M. Lane Prerequisites Distributions, Central Tendency, Variability, Binomial Distribution In the chapter on probability, we saw that the binomial distribution could be used to solve problems such as "If a fair coin is flipped 100 times, what is the probability of getting 60 or more heads?" where x is the number of heads (60), N is the number of flips (100), and π is the probability of a head (0.5). Abraham de Moivre, an 18th century statistician and consultant to gamblers, was often called upon to make these lengthy computations. de Moivre noted that when the number of events (coin flips) increased, the shape of the binomial distribution approached a very smooth curve. Figure 1. de Moivre reasoned that if he could find a mathematical expression for this curve, he would be able to solve problems such as finding the probability of 60 or more heads out of 100 coin flips much more easily. Figure 2.
The T-Test The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever you want to compare the means of two groups, and especially appropriate as the analysis for the posttest-only two-group randomized experimental design. Figure 1 shows the distributions for the treated (blue) and control (green) groups in a study. Actually, the figure shows the idealized distribution – the actual distribution would usually be depicted with a histogram or bar graph. The figure indicates where the control and treatment group means are located. The question the t-test addresses is whether the means are statistically different. What does it mean to say that the averages for two groups are statistically different? This leads us to a very important conclusion: when we are looking at the differences between scores for two groups, we have to judge the difference between their means relative to the spread or variability of their scores.
ONE-WAY ANOVA Analysis of variance (ANOVA) for comparing means of three or more variables. Use this test for comparing means of 3 or more samples/treatments, to avoid the error inherent in performing multiple t-tests Background. If we have, say, 3 treatments to compare (A, B, C) then we would need 3 separate t-tests (comparing A with B, A with C, and B with C). Ideally, for this test we would have the same number of replicates for each treatment, but this is not essential. An important assumption underlies the Analysis of Variance: that all treatments have similar variance. Procedure (see worked example) Don't be frightened by this! Assume that we have recorded the biomass of 3 bacteria in flasks of glucose broth, and we used 3 replicate flasks for each bacterium. Step 1. Step 2. , S x2, and Sd2 (click here for method) Step 3. Step 4. Step 5. and call the sum B. Step 6. Step 7. Step 8. Step 9. Step 10. Step 11. [The total df is always one fewer than the total number of data entries] Step 12. Step 13.
Stats: Two-Way ANOVA The two-way analysis of variance is an extension to the one-way analysis of variance. There are two independent variables (hence the name two-way). Assumptions The populations from which the samples were obtained must be normally or approximately normally distributed. The samples must be independent. The variances of the populations must be equal. Hypotheses There are three sets of hypothesis with the two-way ANOVA. The null hypotheses for each of the sets are given below. The population means of the first factor are equal. Factors The two independent variables in a two-way ANOVA are called factors. Treatment Groups Treatement Groups are formed by making all possible combinations of the two factors. As an example, let's assume we're planting corn. The data that actually appears in the table are samples. Main Effect The main effect involves the independent variables one at a time. Interaction Effect The interaction effect is the effect that one factor has on the other factor. Within Variation
Post Hoc Tests for One-Way ANOVA Post Hoc Tests for One-Way ANOVA (Jump to: Lecture | Video ) Remember that after rejecting the null hypothesis in an ANOVA, all you know is that the groups you compared are different in some way. Imagine you performed the following experiment and ended up rejecting the null hypothesis: Researchers want to test a new anti-anxiety medication. Now that weve rejected the null hypothesis, it is appropriate to perform a Post-Hoc test to discover where the three groups are different. In this lecture, we'll be examining two different tests: Tukey HSD, and Scheffe. The Tukey test is more liberal than the Scheffe test. Tukey HSD With this test, were interested in examining mean differences. Next, we calculate our HSD (Honestly Significant Difference) Figure 4. For this equation, we need MSwithin from our ANOVA source table, as well as our n. Now, we measure how far each mean is from each other mean. Here, we find that all three means are different from one another. Scheffe Reject the null hypothesis.
What Is the Tukey HSD Test? | The Classroom | Synonym on Synonym Experiment designs include many types of tests. Before running an experiment, a researcher must design the experiment, including the tests he wishes to use in the data analysis procedure after the test. In some circumstances, the data analysis indicates that there may be some interesting information that cannot be analyzed through the preplanned tests. In such a case, Tukey's HSD test comes in handy, allowing the researcher to further research the matter even after data has been collected and analysis run. Post-Hoc Tukey's HSD test is a post-hoc test, meaning that it is performed after an analysis of variance (ANOVA) test. Purpose The purpose of Tukey's HSD test is to determine which groups in the sample differ. Procedure Tukey's HSD test works through defining a value known as the Honest Significant Difference (HSD). Strength Like other post-hoc tests, the Tukey HSD test is weak. References "The Collected Works of John W. About the Author Damon Verial has been writing since 2001.