Question: A response variable has two possible explanatory variables, X₁ and X₂. Three linear models are produced - one based on X₁ only, one based on X₂ only, and one based on both X₁ and X₂. The AIC values for each model respectively are, 178.45, 183.56, and 149.21. Which is the best model based on the AIC value? A: Based on X₁ only. B: Based on X₂ only.. C: Based on X₁ and X₂. D: Can't be sure. 13

According to the February 2008 Federal Trade Commission report on consumer fraud and identity theft, 23% of all complaints in 2007 were for identity theft. In that year, assume some state had 534 complaints of identity theft out of 1960 consumer complaints. Do these data provide enough evidence to show that that state had a higher proportion of identity theft than 23%? Test at the 6% level.P: PARAMETER      What is the correct parameter symbol for this problem?                    What is the wording of the parameter in the context of this problem?              H: HYPOTHESES      Fill in the correct null and alternative hypotheses:        ASSUMPTIONS      Since     information was collected from each object, what conditions do we need to check?      Check all that apply. np≥10np≥10 n≥30n≥30 or normal population. σσ is known. σσ is unknown. N≥20nN≥20n n(1−p)≥10

To perform a test to determine whether we have a significant linear relationship using an F test, your friend tells you to compute a two-sided p-value. Is this correct and why?

Use Data Set 7 found on my website for this question. Consider two models: Big and Small. Both models are predicting Exam Tot. Big uses four variables (Female?, Year, Time, and H Total) to predict Exam Tot. Small uses two variables (Time and H Total) to predict Exam Tot. Run a regression for each model. Which one is a better model?     Small is better, but it's arguable that Big is better     Clearly Big     Both are equally good / It's hard to tell.     Big is better, but it's arguable that Small is better     Clearly Small

A survey of high school students was done to examine whether students had ever driven a car after consuming a substantial amount of alcohol (1=yes, 0=no). Data was collected on their sex (male/female), race (White/non-White), and grade level (9,10,11,12). Researchers realized that the impact of race on consuming alcohol before driving might vary by grade level and decided to fit the following model. Variable Coding = 1 if Intercept   Sex () Female Race () Black Grade level ( 9th grade 10th grade 11th grade [Reference = 12th grade]   Attached is the logistic model  1. Compute the OR of drinking before driving for students who self-reported as Black versus non-Black in the 9th grade, adjusting for gender. 2. Compute the OR of drinking before driving for students who self-reported as Black versus non-Black in the 12th grade, adjusting for gender. 3. Compute the OR of drinking before driving for someone in the 9th grade versus 12th grade for a student who…

The owener of a winery collects data on competing wineries every year.  He would like to predict the gross sales (in number of cases) from the size of the wineries (in acres). What is the explanatory variable in this study?

The relationship between IQ level and exam score was estimated as below in a linear format. (IQ level and exam scores are both measured in points)   Exam score = 1.38 * IQ level - 67.43 (The p-value for the coefficient of IQ level is 0.04.)   Base on the above equation, which of the following statements are true? (Select all that apply. There can be more than one answer.) The expected exam score of a person with an IQ level of 100 points is 70.57 The expected exam score of a person with an IQ level of 0 points is 1.38 points As a person’s IQ level increases by 1 point, his or her exam score is expected to increase by 1.38

To monitor and improve its productivity, a company made an investigation and found out that the factor that affects the productivity the most is the absenteeism. The company data analytics department have collected data about the two variables (Productivity and Absenteeism) for the 12 past years as shown in the table below. Now the purpose of the company is to determine, through regression analysis, whether the productivity is statistically affected by the absenteeism level or not. Year Absenteeism Productivity (in number of absent worker) (in Million AED) 1 204 342 2 352 336 3 154 406 4 206 410 5 422 278 6 530 214 7 750 138 8 482 268 9 374 262 10 120 356 11 188 396 12 634 152 Questions: Construct a scatter diagram for the data about productivity and absenteeism then interpret the possible relationship that can be found. Construct a simple regression model to predict the…

Consider a linear regression model that relates school expenditures and family background to student performance in Massachusetts using 224 school districts. The response variable is the mean score on the MCAS (Massachusetts Comprehensive Assessment System) exam given in May 1998 to 10th-graders. Four explanatory variables are used: (1) STR is the student-to-teacher ratio, (2) TSAL is the average teacher’s salary, (3) INC is the median household income, and (4) SGL is the percentage of single family households. The Excel Regression output for the sample regression equation is given below. (a) What proportion of the variation in MCAS score is explained by the explanatory variables?    (b) At the 5% level, are the explanatory variables jointly significant in explaining MCAS score? Explain briefly.     (c) At the 5% level, which variables are individually significant at predicting MCAS score? Explain briefly.       (d) Suppose a second regression model (Model 2) was generated using only…

Consider a linear regression model that relates school expenditures and family background to student performance in Massachusetts using 224 school districts. The response variable is the mean score on the MCAS (Massachusetts Comprehensive Assessment System) exam given in May 1998 to 10th-graders. Four explanatory variables are used: (1) STR is the student-to-teacher ratio, (2) TSAL is the average teacher’s salary, (3) INC is the median household income, and (4) SGL is the percentage of single family households. The Excel Regression output for the sample regression equation is given below. (a) What proportion of the variation in MCAS score is explained by the explanatory variables?    (b) At the 5% level, are the explanatory variables jointly significant in explaining MCAS score? Explain briefly.     (c) At the 5% level, which variables are individually significant at predicting MCAS score? Explain briefly.       (d) Suppose a second regression model (Model 2) was generated using only…