The ability to sense the movement of different body parts (for example to know that I moved my hand towards my face) is called:

An economist working for a large retail chain estimates the following multiple regression model for daily cold beverage sales: Q=500-20P+ 5T  Q is the predicted quantity of units sold per day, P is the price per unit (in dollars), and T is the daily high temperature (in degrees Fahrenheit). The model has an R-squared value of 0.81. What does this R-squared value mean in this context?

The following table was generated using the correlation function in Excel. The independent variables included the number of bathrooms, the square footage, and the number of bedrooms. Are there concerns between any variables for multicollinearity? Please be specific about which variables, if any, and use the data to justify your answer.     Sellling Price Baths Sq Ft. No. of Bedrooms Sellling Price 1 Baths 0.451662044 1 Sq Ft. 0.659911692 0.564953218 1 No. of Bedrooms 0.54042771 0.22247966 0.452893403 1

Using the standard error found in question #26, calculate the confidence interval with x* (x-star) = 58 and alpha = .05. SUMMARY OUTPUT Regression Statistics Multiple R 0.93686171 R Square 0.87770986 Adjusted R Square 0.86242359 Standard Error 7.52312475 Observations 10 ANOVA   df SS MS Regression 1 3249.72075 3249.72075 Residual 8 452.779248 56.597406 Total 9 3702.5     Coefficients Standard Error t Stat Intercept 5.847009 7.9717307 0.73346795 Hours 0.82953944 0.10947433 7.57747929    

For the following scenario determine which the independent and dependent variables: (Type either independent or dependent in the blank) a. Sales [dependent], Advertising Budget [Independent]

In regression analysis with more than one independent variable, when is the t test statistic used and what does its p-value mean? 

An economist working for a large retail chain uses the following multiple regression model to estimate daily cold beverage sales: Q=500-20P+ 5T  Q is the predicted quantity of units sold per day, P is the price per unit (in dollars), and T is the daily high temperature (in degrees Fahrenheit). What is the predicted number of units sold on a day when the price is $5 and the temperature is 95°F?

Data collected for an auto repair shop includes the type of repair. Repairs were listed as mechanical, electrical, maintenance, and tire replacement. You are trying to determine if the cost of the repair depends on the type. How would you include this categorical data in a regression model? Be specific and indicate what the columns would look like in Excel. How many columns would you need?

The following estimated regression equation has been proposed to predict sales at a fast food outlet.  y = 18 – 2×1 + 7×2 + 15×3 ​where x1 = the number of competitors within 1 mile x2 = the population within 1 mile (in 1000s) x3 = 1 if drive-up windows are present; 0 otherwise  = estimated sales (in $1000s)   Predict sales for an outlet with 2 competitors, a population of 10,000 within one mile, and no drive-up window (give the answer in dollars).   

An economist working for a large retail chain estimates the relationship between the price of a product and the quantity sold using simple linear regression. The estimated equation is: Q=500-20P where: Q​ is the predicted quantity of units sold per week, and P is the price per unit (in dollars). What is the correct interpretation of the slope in this equation?