Finance Majors Business Analytics Majors n= 140 n2 = 30 X, = $48,137 X2 = $55,217 s, = $19,000 S2 = $10,000 Formulate hypotheses so that, if the null hypothesis is rejected, we can conclude that salaries for Finance majors are significantly lower than the salaries of Business Analytics majors. Use a = 0.05. (Let u, = the population mean salary for Finance majors, and let µ = the population mean salary for Business Analytics majors. Enter != for + as needed.) What is the value of the test statistic? (Use u, - H3. Round your answer to three decimal places.) What is the p-value? (Round your answer to four decimal places.) p-value =

17. Each full carton of Grade A eggs consists of 1 randomly selected empty cardboard container and 12 randomly selected eggs. The weights of such full cartons are approximately normally distributed with a mean of 840 grams and a standard deviation of 7.9 grams. a. What is the probability that a randomly selected full carton of Grade A eggs will weigh more than 850 grams? The weights of the empty cardboard containers have a mean of 20 grams and a standard deviation of 1.7 grams. It is reasonable to assume independence between the weights of the empty cardboard containers and the weights of the eggs. It is also reasonable to assume independence among the weights of the 12 eggs that are randomly selected for a full carton. Let the random variable X be the weight of a single randomly selected Grade A egg. b. What is the mean of X? C. What is the standard deviation of X?

Heights (cm) and weights (kg) are measured for 100 randomly selected adult males, and range from heights of 139 to 188 cm and weights of 38 to 150 kg. Let the predictor variable x be the first variable given. The 100 paired measurements yield x = 167.62 cm, y = 81.37 kg, r 0.113, P-value = 0.263, and y = - 105+1.01x. Find the best predicted value of y (weight) given an adult male who is 142 cm tall. Use a 0.05 significance level. %3D The best predicted value of y for an adult male who is 142 cm tall is kg. (Round to two decimal places as needed.)

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IS the following statment true or false, please explain why    For each x term in the multiple regression equation, the corresponding β is referred to as a partial regression coefficient.

Below are bivariate data O each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the data and the least-squares regression line. The equation for this line is ing birthrate and life expectancy information for y = 81.87 – 0.46x. Birthrate, x (number of births per 1000 pop.) Female life expectancy, y (in years) 85- 35.7 67.7 80- 41.5 63.9 75 31.9 63.3 19.9 73.0 70 50.5 60.4 65. 24.4 72.7 60- 50.1 63.2 55 13.8 72.5 50 50.3 54.6 45.6 57.9 15.9 76.2 Figure 1 26.6 71.9 Send data to Excel

We are researching the charity donation behaviour of Australians. We have the following model: (E1) Donate = β0 + β1Income +β2Avg_Gift + β3Edu + u Where: Donate is a dummy variable equal to 1 if the individual makes a donation in response to a social media campaign by our charity organisation, and 0 otherwise Income is the annual household income Avg_Gift is the average value of past donations made by the individual to our charity Educ is the individual's level of education (in years) Our data is a random sample of the population and we have 1,614 observations. You should assume E [u | Income, Avg_Gift, Educ] = 0. Using the information above, please answer the following 3 questions. [i] Referring to Model (E1) above, interpret the coefficient β1.  [ii] You know that this model will suffer from heteroskedasticity. Why is this the case? Explain your reasoning.  [iii] In your own words, what is heteroskedasticity?