Assignment 4 Essay

Topics Distribution of the sample mean. Central Limit Theorem. Confidence intervals for a population mean. Confidence intervals for a population proportion. Sample size for a given confidence level and margin of error (proportions). Poll articles. Hypotheses tests for a mean, and differences in means (independent and paired samples). Sample size and power of a test. Type I and Type II errors. You will be given a table of normal probabilities. You may wish to be familiar with the follow formulae and their application.

7. The data set for this problem can be found through the Pearson Materials in the Student Textbook Resource Access link,

Since Dr. Williams does not know the population standard deviation for all his Intro Psych. classes combined, he should use a t-test. Specifically, he should use the t-test for two independent means. The reason he should use this test is because the data uses interval/ ratio scores, there are two independent samples being compared as the experiment utilized a between subjects design, and it is assumed that the data is normally distributed. Also, Dr. Williams is comparing the two classes to each other, not both classes to the population. If he were comparing both classes as a whole sample to the entire population of Intro. Psych students, then it would be better to use a single sample t-test.

At the .01 significance level is there a difference in the mean amount purchased on an impulse at the two stores? Explain these results to a person who knows about the t test for a single sample but is unfamiliar with the t test for independent means.

Construct the indicated confidence interval for the difference between the two population means. Assume that the assumptions and conditions for inference have been met. 13) The table below gives information concerning the gasoline mileage for random samples of trucks of two different types. Find a 95% confidence interval for the difference in the means m X - m Y. Brand X Brand Y 50 50 20.1 24.3 2.3 1.8 13)

[pic] [pic] [pic] [pic] [pic] One-Sample Z The assumed standard deviation = 1232 N Mean SE Mean 95% CI 50 4559 174 (4218, 4900) One-Sample T N Mean StDev SE Mean 95% CI 50 4559 1232 174 (4209, 4909)

The second step is defining the significance level, determining the degrees of freedom and finding the critical value. The a-level shows that for a result to be statistically significant, it cannot occur more than the a-level percentage of time by chance. The critical value can be obtained by using the t-test table. The degrees of freedom is

Now we need to check whether or not the calculated chi-square probability, p is less than or equal to α = 0.05 OR the Chi-Square value, χ2 from chi-square distribution tables is equal to or greater than α = 0.05. If p α OR χ2 α, we reject the null hypothes is and conclude that at the 0.05 significance level, there is a relationship between the row variables and column variables. Since p = 0.04285 < α = 0.05 (OR χ2 = 11.1 > α = 0.05) we conclude

MULTIPLE CHOICE 1. value of a. b. c. d. ANS: A 2. a. b. c. d. ANS: A 3. correlation a. b. c. d. ANS: C 4. a. b. c. d. ANS: D 5. The mathematical equation relating the independent variable to the expected value of the dependent variable; that is, E(y) = β0 + β1x, is known as a. regression equation b. correlation equation c. estimated regression equation d. regression model ANS: A 6. a. b. c. d. ANS: C 7. a. b. c. d. In regression analysis, the unbiased estimate of the variance is coefficient of correlation coefficient of determination mean square error slope of the regression equation The model developed from sample data that has the form of is known as regression equation correlation equation estimated

Ho: There is not a significance difference between the alive number of balls collected on high vs. low resource abundance.

c. After conducting the post hoc comparison, it is determined that a difference of 1.91 or more at the .05 alpha level.

The goal of this experiment is to collect measureable data from two different populations and conclude whether there is a difference between the means of the two populations. The two populations chosen for this study were male and female students who attend Montclair State University. The variable tested for this study was the number of songs from a random sample of males and females. To collect the random samples from the two populations, I approached various students on campus and asking them for the number of songs on their phone or listening device. I approached students in numerous places on campus such as the Student Center, the Sprague Library, University Hall, Car Parc

The null hypothesis suggests that there is no difference between the means of the three samples, while the claim in the alternative hypothesis suggests that at least one mean is different.

In Chapter 9, we learned how to conduct a t test of a hypothesis when we were testing the mean of a single sample group against some pre-determined value (i.e., the 21.6 gallons of milk consumption as the national average). This week, in Chapter 10, we will see how to test hypotheses that involve more than one sample group—such as testing to see if males are significantly taller than females. If we have two groups, then the technique that we will use will still be a t test. If we have more than two groups, then we will have to use a different test called Analysis of Variance (ANOVA, for short).

The t-test can be used to test whether two groups are different. The independent t-test is used when there are two experimental conditions (e.g. male and female or public and private banks) and different participates have been used in each condition (e.g. SECI and innovation processes). Independent t-test table yielded by PASW normally mentions two rows for the test statistics: one row is labelled Equal variance assumed, while the other is labelled Equal variances not assumed. Which row is considered is based on the significant of Levene's test for Equality of Variances. Therefore, if the Levene’s test is significant, the Equal variances not assumed row will be used for the t-test. The significant of this difference is based on the significant of the t-test. That is, if this test is significant, it could be included that the difference between the means of these