Write two main differences between Forecasting and Time-Series Analysis (TSA)? ii- Is forecasting technique more accurate than TSA? Justify your answer. iii- If you were to go buy a car and are provided with the following data sets (standard deviation and Variance). Which set of data would help you make a better decision? And why? iv- Find the p-value if Z < -.90, alpha = 0.05 (one-tail, lower tail) v- Find P-value in a left tailed test if the test statistics is z = -1.87

Consider the following time series data. Week 1 2 3 4 5 6 Value 17 13 17 10 18 15 Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. (a) mean absolute error MAE= (b) mean squared error MSE= (c) mean absolute percentage error (Round your answer to two decimal places.) MAPE= % (d) What is the forecast for week 7? Need Help? Read It Watch It Type here to search W L3 72°F B N M

Graduation Rates. Regarding the relationship between college graduation rate and the predictor variables student-to-faculty ratio, percentage of freshmen in the top 10% of their high school class, and percentage of applicants accepted. a. Perform a residual analysis to assess the assumptions of linearity of the regression equation, constancy of the conditional standard deviation, and normality of the conditional distributions. Check for outliers and influential observations. b. Does your analysis in part (b) reveal any violations of the assumptions for multiple regression inferences? Explain your answer.

2. Which measures of central tendency can be used for the variable below? Explain why each of the three measures of central tendency is or is not applicable. Answer Here: How closely do you follow the news about current events? Would you say... Cumulative Frequency Percent Valid Percent Percent Valid Most of the time 1128 41.9 41.9 41.9 some of the time 1304 48.4 48.5 90.4 rarely 239 8.9 8.9 99.3 never 19 .7 .7 100.0 Total 2690 99.9 100.0 Missing Don't Know/Refused (DON'T READ) 2 .1

A company that develops an automated customer service model is interested in knowing whether two versions, Version A and Version B, will get different ratings from customers. Participants in a focus group are taken through samples from both versions, then take a survey to rate each version. A summary of the data obtained from the study is given below. Assume ratings from the different surveys generally have the same standard deviation. Mean Observations Pearson Correlation Hypothesized Mean Difference df t Stat P(T<=t) one-tail t Critical one-tail P(T<=t) two-tail t Critical two-tail Confidence Level Version A Version B 33.133 28.67 15 15 -0.325 0.00 14 n = Ex: 9 -1.426 0.088 -1.345 0.176 -1.761 95% Degrees of freedom: df Point estimate for Version A: XA = Ex: 1.234 Point estimate for Version B: x B = 1 = من t = −1 Ex: 1.234 0 1 t = P = = Ex: 1.234 2 3 2

Please correct my answer for part c

Part 2 of 5 What is the value of the test statistic? Enter the value to the full accuracy shown (do not round). Part: 2/ 5 Part 3 of 5 What is the P-value? Enter the value to the full accuracy shown (do not round). P-value

When you do not have the luxury of centering the statistics, you may need to define a non-centrality parameter. How would you do this, and why? Provide an example, including detailed explanations, formulas, and necessary visuals.

A statistical program is recommended. A company that sells musical instruments has been in business for five years. During that time, sales of pianos increased from 12 units in the first year to 76 units in the most recent year. The firm's owner wants to develop a forecast of piano sales for the coming year. The quarterly sales data follow. Year Quarter 1 Quarter 2 Quarter 3 Quarter 4 1 2 3 4 5 4 7 11 12 18 2 4 forecast for quarter 2 forecast for quarter 3 forecast for quarter 4 3 9 10 1 4 5 7 13 5 14 16 22 35 Total Yearly Sales 12 29 35 50 76 (a) Use the following dummy variables to develop an estimated regression equation to account for any seasonal and linear trend effects in the data: x₁ = 1 if quarter 1, 0 otherwise; x₂ = 1 if quarter 2, 0 otherwise; and x3 = 1 if quarter 3, 0 otherwise. (Let t = 1 denote the time series value in quarter 1 of year 1; t = 2 denote the time series value in quarter 2 of year 1; ... and t = 20 denote the time series value in quarter 4 of year 5. Round…