SCM 200 Exam 3 True/False Questions

T/F: The slope of a linear regression equation is an example of a correlation coefficient

False

T/F: The y-intercept, b0, in a regression equation can never be less than 0

False

T/F: We know the slope of a regression line is positive when the coefficient of determination R-Square is positive

False

T/F: When dealing with multiple regression models, adjusted R Square will usually be larger than unadjusted R

False

T/F: A multiple linear regression has multiple predictor variables

True

T/F: A researcher is conducting a two-tailed hypothesis test with 10 degrees of freedom. He used sample data to calculate the value of the test statistic to be t=1.372. This means the p-value will be equal to 0.20

True

T/F: A z-score of 2.20 was calculated in an upper-tailed test of the difference of two proportions. The p-value for this test is 0.0139

True

T/F: As the value of R squared approaches 1, the relationship between the x and y becomes stronger

True

T/F: Dummy variables are a way to incorporate qualitative (categorical) data into a linear regression model

True

T/F: If R-Squared=1, it means the linear regression model explains all of the variation on the data table

True

T/F: If r > 0, then the b1 slope, must be positive

True

T/F: If we were forecasting the trend in sales from time series data with a simple linear regression equations, the independent variable would be time

True

T/F: If you accept the null hypothesis that the population slope is equal to zero, you can conclude that the X variable is not a statistically significant predictor

True

T/F: R-Squared can also be referred to as the coefficient of determination

True

T/F: The error term in the regression model describes the effects of all factors OTHER THAN the independent variables on y

True

T/F: The error term is defined as the difference between the original data value (y) and estimated values of y (y-hat)

True

T/F: The sampling distribution of x-bar1 - x-bar2 is normal for large samples

True

T/F: When dealing with a multiple regression model, an adjusted R-Square value can NEVER be greater than the corresponding unadjusted R Square Value

True

T/F: When dealing with time series analysis, observations must be taken at regular intervals overtime

True

T/F: When the correlation coefficient is equal to -1.0, the standard error of the estimate (Se) must equal zero

True

What is the expected value of the error term in regression?

0

T/F: The coefficient determination can take any value between -1 and +1

False

T/F: A correlation of +0.75 is stronger than a correlation of -0.91

False

T/F: A multiple linear regression model has multiple response variables

False

T/F: Another name for a histogram is a scatterplot

False

T/F: Even though this is not common, it is possible to have a data set where the slope linear regression equation is positive and at the same time the correlation coefficient is negative

False

T/F: If x and y are correlated in a regression, we can concede that x causes y

False

T/F: In a regression equation, the slope b1 is always positive

False

T/F: It is never possible for regression coefficients to be less than -1

False

T/F: SD of sales = $5.50 SD of marketing spending = $3.95 Correlation = 0.88 If we are predicting marketing spending from sales, the regression coefficient is 0.78

False

T/F: We know the slope of a regression line is positive when the coefficient of determination R-Square is positive

False--the coefficient of determination is ALWAYS POSITIVE (between 0 and 1) The correlation coefficient tells you about the slope of the regression equation because its value can either be positive or negative

T/F: A multiple linear regression model has multiple response variables

False--the response variable is the y-variable. There is only one y-variable in a multiple linear regression. There are MULTIPLE x variables

T/F: A correlation of +0.75 is stronger than a correlation of -0.91

False--the strongest correlation is the correlation with an ABSOLUTE VALUE closest to 1

The _____ the R Square, and the _____ the standard error of the estimate (Se), the stronger the relationship between the dependent variable and the independent variable

Higher;Lower

Which component of time series analysis consists of erratic and unsystematic fluctuation in time series data

Random Variation

The component of time series analysis that refers to fluctuations associated with climate, holidays, and related activities

Seasonality

Which is the quantity that gives the average in y for a one unit change in x when performing a simple linear regression?

Slope of regression line

When evaluating a regression, for a value of independent variable x, the confidence interval for the average value of the dependent variable y is _______ the prediction interval for an individual value

Smaller than

What explains how the standard of error of the estimate Se differs from the coefficient of determination?

The S.E. of the estimate keeps the original data units of y

T/F: When the population correlation coefficient is -0.95, there is a strong inverse relation in the population; however, when the value of the population correlation coefficient is 0, there is no linear relationship

True

T/F: Whenever the slope of the linear regression equation is equal to zero, the correlation coefficient must be equal to zero

True

T/F: If r < 0, then the b1, the slope, must be negative

True

T/F: When dealing with a multiple regression model, an adjusted R-Squared can NEVER be greater than the corresponding unadjusted R-Square values

True

T/F: A researcher is conducting a two-tailed hypothesis test with 10 degrees of freedom. He used sample data to calculate the value of the test statistic to be t=1.372. This means the p-value will be equal to 0.20

True--make sure to DOUBLE the value found using the t-table because the problems days that it is a two-tailed test

When using a regression model for forecasting the NEXT value of y, the prediction interval will be..

Wider than the confidence interval for the mean

What measures the strength of the linear relationship between the dependent and the independent variable in a regression?

correlation coefficient