Assume that data are available on other characteristics of the subjects that are relevant to determining the randomized experimental outcome. Then not including these determinants explicitly results in: a. the limited dependent variable model. b. omitted variable bias. С. the multiple regression model. Od. large scale equilibrium effects. е. All of the above. Of. None of the above.

Assume that data are available on other characteristics of the subjects that are relevant to determining the randomized experimental outcome. Then not including these determinants explicitly results in: a. the limited dependent variable model. b. omitted variable bias. С. the multiple regression model. Od. large scale equilibrium effects. е. All of the above. Of. None of the above.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter3: Straight Lines And Linear Functions

Section3.4: Linear Regression

Problem 12SBE: Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4

See similar textbooks

Similar questions

Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
9) For the same set of observations on a specified dependent variable, two different independent variables were used to develop two separate simple linear regression models. A portion of the results is presented below. R² (R-squared) Model 1 0.92 Based on R2 of each model, which model is more useful? Why? Model 2 0.85
MLR. 3. rules out certain relationships between explanatory variables and MLR. 4. rules out relationships between unobservables and regressors. True False QUESTION 7 OF 10 If an independent variable in a multiple linear regression model is an exact linear combination of other independent variables, the model suffers from the problem of _____. SELECT ALL CORRECT OPTIONS OPTION A perfect collinearity OPTION B omitted variable bias OPTION C heteroskedasticty OPTION D homoskedasticity QUESTION 8 OF 10 The estimate σ^σ^ is interesting because it is an estimate of the standard deviation of the unobservables affecting y. In other words, it estimates the standard deviation in yy after the effect of xx has been taken out and is called the standard error (s.e.). True False QUESTION 9 OF 10 Larger the sampling variance in xx in an SLR model, larger the sampling variance for OLS estimators. True False QUESTION 10 OF 10 The regression model,…
11. Testing multiple exclusion restrictions with the test and R-squared figures Suppose your friend Paolo needs your assistance testing five exclusion restrictions in a multiple linear regression model at the 95% level. Specifically, his null hypothesis is that B6 ,..., B10 are all equal to 0, after controlling for the independent variables r1 ,.., 15. The original, unrestricted model consisted of 10 population slope parameters, B1 ,.., B10, plus an intercept parameter Bo and was estimated using a random sample of 470 observations. You ask Paolo for the residual sum of squares from the restricted and unrestricted models. He says he lost those figures, but he did save the R- squared values. The R-squared for the unrestricted model is 0.911, while the R-squared for the restricted model is 0.892. Since the unrestricted model has degrees of freedom, while the restricted model has degrees of freedom, you know that the number of exclusion restrictions must be q= fail to reject Note: The…
You have estimated a multiple regression model with 6 explanatory variables and an intercept from a sample with 46 observations. What is the critical value of the test statistic (tc) if you want to perform a test for the significance of a single right-hand side (explanatory) variable at α = 0.05? a.) 2.023 b.) 2.708 c.) 2.423 d.) 2.704
Below is an attempt at describing the simple linear regression model, which is incomplete and contains errors: A simple linear regression model is given by the formula Y; 1,..., n, where a and B are the parameters, the x; is the explanatory variable and the Y; is the response random variables which are all independent and identically distributed. = a + Bx; for i Identify and explain the three mistakes that have been made.
The data in the table represent the number of licensed drivers in various age groups and the number of fatal accidents within the age group by gender. Complete parts (a) to (c) below. Click the icon to view the data table. C... (a) Find the least-squares regression line for males treating the number of licensed drivers as the explanatory variable, x, and the number of fatal crashes, y, as the response variable. Repeat this procedure for female Find the least-squares regression line for males. ŷ=0x+0 (Round the slope to three decimal places and round the constant to the nearest integer as needed.) Data for licensed drivers by age and gender. 21-24 25-34 35-44 45-54 55-64 65-74 > 74 Number of Male Fatal Licensed Age Drivers (000s) < 16 12 16-20 6,424 6,914 18,068 20,406 Number of Number of Female Fatal Crashes Licensed (Males) Drivers (000s) 227 12 6,139 Crashes (Females) 77 2,113 1,534 5,180 5,016 6,816 8,567 17,664 2,780 7,990 20,047 2,742 19,984 14,441 8,386 5,375 19,898 14,328 8,194…
The following table gives information on the amount of sugar (in grams) and the calorie count in one serving of a sample of 6 varieties of granola bars.Sugar (grams) 4 12 8 7 14 6 Calories 120 140 120 180 190 80 a. Decide which variable should be the independent variable and which should be thedependent variable. Justify your decision. (2 marks) e. Write down the regression equation. (1 mark)f. Find and interpret the coefficient of determination. (3 marks)g. Use the 10% level of significance to examine whether the independent variable is a goodpredictor for the dependent variable. In responding, state the null hypothesis(es), thedecision rule and make the decision for each hypothesis. (5 marks)h. Would the simple regression be useful in predicting calories given the amount of sugar of 5 grams? In responding, state the null hypothesis(es), the decision rule for α = 0.05 and make the decision for each hypothesis. (5 marks)
A property agent wants to study the relationship between the age of a house (X) and its selling price (Y). Listed below is a random sample of houses that the agent has sold recently. [House] [Age in Years (X)] [Selling Price in $'m (Y)] 1 9 2.2 2 7 1.9 3 11 1.7 4 12 0.8 5 8 2.5 a) Determine the linear regression equation. b) i. If the age of a house is 10 year-old, what is the selling price? ii. If the age of a house is 20 year-old, what is the selling price? c) i. Determine the correlation coefficient. ii. Interpret the result.
22. You are given the following value from a t-table as needed: P(tı40 > 1.656) = 0.05 Association between student math and verbal scores on the SAT was modeled by a linear regression model using the output given below for sample scores for a graduating class from a high school. Variable Count Мean Verbal 142 596.296 Math 142 612.099 Dependent variable is Math R-square = 46.9% %3D s=71.75 with 142-2=140 df Variable Coefficient SE(Coeff) t-ratio P-value Intercept 209.554 34.35 6.1 <0.0001 Verbal 0.675 0.0568 11.9 <0.0001 Find a 90% prediction interval for the Math score of a graduating student that has a verbal score of 500.
The accompanying data file contains 40 observations on the response variable y along with the predictor variables x and d. Consider two linear regression models where Model 1 uses the variables x and dand Model 2 extends the model by including the interaction variable xd. Use the holdout method to compare the predictability of the models using the first 30 observations for training and the remaining 10 observations for validation. Click here for the Excel Data File a-1. Use the training set to estimate Models 1 and 2. Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places. Predictor Variable Model 1 (No interaction) Constant X d xd RMSE a-2. Calculate the RMSE of the two models in the validation set. Note: Do not round intermediate calculations and round final answers to 2 decimal places. Model 2 2.00 4.18 41.52 0.00 Model 1 (No interaction) Model 2 (Interaction) 9.01 because its RMSE is Model 2 (Interaction) a-3. Which model is better for…
A sales manager has collected the following data on annual sales (y) and years of experience (x) . Sales person Years of Experience (x) Annual Sales (K’000) (y) 1 80 3 97 4 92 4 102 6 103 6 8 111 10 119 10 123 11 117 13 136 Draw a scatter diagram. Does a linear relationship between x and y seem appropriate Estimate the simple linear regression line. Interpret the parameters in the model (c) What practical use could be made of this equation? Use the estimated regression equation to predict annual sales for a sales man with 9 years of experience At the 5% level of significance would…