![Chapter 23 – Inferences About Means](http://s1.studyres.com/store/data/000922813_1-efffddf3314433be0f30a584406e3b2a-300x300.png)
Chapter 6
... Procedure for Using a Normal Distribution to Approximate a Binomial Distribution 1. Establish that the normal distribution is a suitable approximation to the binomial distribution by verifying np 5 and nq 5. 2. Find the values of the parameters µ and by calculating µ = np and = npq. 3. Iden ...
... Procedure for Using a Normal Distribution to Approximate a Binomial Distribution 1. Establish that the normal distribution is a suitable approximation to the binomial distribution by verifying np 5 and nq 5. 2. Find the values of the parameters µ and by calculating µ = np and = npq. 3. Iden ...
Lecture 4
... The Bonferroni procedure described in the previous section is the most general approach to the multiple sample analysis of group differences in that it makes few assumptions on the data. If one is willing to make more assumptions about the data, other methods exist for analyzing the data which if th ...
... The Bonferroni procedure described in the previous section is the most general approach to the multiple sample analysis of group differences in that it makes few assumptions on the data. If one is willing to make more assumptions about the data, other methods exist for analyzing the data which if th ...
Course Review Chapter 9 Testing Hypotheses
... From the t distribution table we can conclude that our sample mean is statistically significant from the general population mean because: 1. On page 520 of your book we can see that, for 60 degrees of freedom, a t statistic stat st c of 1.671 .67 has a p value of .05 when using a one-tailed test. Th ...
... From the t distribution table we can conclude that our sample mean is statistically significant from the general population mean because: 1. On page 520 of your book we can see that, for 60 degrees of freedom, a t statistic stat st c of 1.671 .67 has a p value of .05 when using a one-tailed test. Th ...
2 Sample t-Test (unequal sample sizes and unequal
... Let’s now calculate our confidence limits (basic equations not shown): LL = (23.565 − 30.28) − 2.000 *1.2967 = −9.308 LU = (23.565 − 30.28) + 2.000 *1.2967 = −4.122 We see that both bounds are negative numbers indicating that they do not encompass zero, therefore the hypothesis that there is no diff ...
... Let’s now calculate our confidence limits (basic equations not shown): LL = (23.565 − 30.28) − 2.000 *1.2967 = −9.308 LU = (23.565 − 30.28) + 2.000 *1.2967 = −4.122 We see that both bounds are negative numbers indicating that they do not encompass zero, therefore the hypothesis that there is no diff ...
Weight of Evidence Formula Guide
... For continuous predictors, first a default coding is derived using the Classification and Regression Trees (C&RT) algorithm. For default categories with fewer than 20 groups STATISTICA will explicitly search through all possible combinations of default groups to achieve the least numbers of groups w ...
... For continuous predictors, first a default coding is derived using the Classification and Regression Trees (C&RT) algorithm. For default categories with fewer than 20 groups STATISTICA will explicitly search through all possible combinations of default groups to achieve the least numbers of groups w ...
The normal distribution, estimation, confidence intervals.
... can translate any instance of it into a standardized form. This is because of the scaling relations we discussed when we discussed the proof of the CLT. ● For each value, subtract the mean and divide by the standard deviation. ● This gives us the standard normal distribution which has μ = 0 and σ = ...
... can translate any instance of it into a standardized form. This is because of the scaling relations we discussed when we discussed the proof of the CLT. ● For each value, subtract the mean and divide by the standard deviation. ● This gives us the standard normal distribution which has μ = 0 and σ = ...
Class2
... General process of making inference about a statistic (1) Establish the sampling distribution of the statistic to assess the variability of the statistic. For example, if we are interested in the mean reading score of students in Taipei, we take a sample and compute the sample mean. Because this sa ...
... General process of making inference about a statistic (1) Establish the sampling distribution of the statistic to assess the variability of the statistic. For example, if we are interested in the mean reading score of students in Taipei, we take a sample and compute the sample mean. Because this sa ...
Quiz 9
... Exploring the Distribution of Sample Means by Computer Simulation This is an individual assignment. You are allowed to seek help from persons other than me for programming questions only. I reserve the right to verbally question you about your responses and assign a grade of zero if it becomes appar ...
... Exploring the Distribution of Sample Means by Computer Simulation This is an individual assignment. You are allowed to seek help from persons other than me for programming questions only. I reserve the right to verbally question you about your responses and assign a grade of zero if it becomes appar ...