A researcher wants to include the variable "skill" in a regression, but cannot find a proper metric tomeasure it. If the researcher just decides not to include it in the model, it will...O a. Lead to biased estimates of the variance.O b. Cause multicollinearity.O c. Lead to biased least squares estimators.O d. Cause heteroscedasticity. Which of the following is not a good interpretation of a seasonal index of 1.26 for fall quarter sales?O a. Fall quarter sales are 26 perbent higher than the annual average.O b. Fall quarter sales represents 1.26 times the annual average.O c. Fall quarter sales represents an increase of 26 percent over last year's annual average.O d. Fall quarter sales average 126 percent of annual sales.

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Consider the following data. X represents the independent variable and Y represents the dependent variable. Suppose we wish to construct a least square regression line. Question 1) Calculate B1 with triangle over top. 0.525 0.425 0.725 0.625 Question 2) Calculate B0 with triangle over top. 4.98 4.88 5.36 4.4 Question 3) What is the least squares regression line. Y = 4.98 + 0.425X Y = 0.425 + 4.98X Y = 0.525 + 4.88X Y = 4.88 + 0.525X X 0 3 4 5 12 Y 8 2 6 9 12
In order to examine the relationship between the selling price of a used car and its age, an analyst uses data from 20 recent transactions and estimates Price = Be + B1Age+ e. A portion of the regression results is shown in the accompanying table. Standard Error 734.41 p-Value 1.51E-16 2.02E-08 -1,203.251 126.91 a. Specify the competing hypotheses in order to determine whether the selling price of a used car and its age are linearly related. Intercept Age Coefficients 21,258.96 Test statistic 0; versus HA -9.481 t Stat 28.947 b. Calculate the value of the test statistic. Note: Negative value should be indicated by a minus sign. Round your answer to 3 decimal places. 10
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage. v. The une ory tor ine most recent m odel year. Complete parts (a) through (d) accompanying data represent the weights of various domestic cars and their miles per gallon in BEE Click here to view the weight and gas mileage data (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. y = 0.00702 * + (43.2) (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. Choose the correct answer below and fill in any answer boxes in your choice.answer trom part a to find this answer O A. For every pound added to the weight of the car, gas mileage in the city will decrease by mile(s) per gallon, on average. A weightless car will get miles per gallon, on averag B. For every pound added to the weight of the car, gas…
Answer true or false to each of the following statements and explain your answers. a. Multicollinearity occurs when two or more of the predictor variables are highly intercorrelated. b. The variance inflation factor, VIF, for a predictor variable cannot change as we add other predictor variables to the regression equation. c. If the points in a three-dimensional scatterplot of predictor variables x1, x2, and x3 lie approximately on a line or a plane, the VIF s for these predictor variables will be close to zero.
Two measures of a baseball player's effectiveness as a hitter are the number of hits he makes in a season and thenumber of times he "bats in" a run (knows as "Runs Batted In" or RBIs). Can we predict a batter's RBIs from hisMajor League Baseball batters in 2017.hits? Below is numerical and graphical output from a computer regression of RBIs on Hits for 12 randomly selected Major League Baseball batters in 2017. Assume that the conditions for inference have been satisfied.(a) Do these data provide convincing evidence that there is a linear relationship between RBIs and Flits for MajorLeague Baseball batters in 2017? (b) Construct a 95% confidence interval for the slope of the population regression line for predicting RBIs from Hits.
QUADRATIC REGRESSION 2) Road & Track provides the following sample tire wear and maximum load capacity for automobile tires. (IMG 1) A. Find the coefficient of determination. comment on it B. Find the equation of the regression parabola and calculate the estimated data for each value of the independent variable. C. Determine the residual variance, the standard error of estimate, and the explained variance. comment them
1. What is the greatest concern about the regression below? a) It has a small slope. b) It has a high . c) The investigator should not be using a linear regression on these data. d) The residuals are too large. e) The regression line does not pass through the origin. 2. The scatterplots below display three bivariate data sets. The correlation coefficients for these data sets are 0.03, 0.68, and 0.89. Which scatter plot corresponds to the data set with = 0.03? a) Plot 1 b) Plot 2 c) Plot 3
7. One of advantages of multiple regression is that it allows us to examine the effect of an exposure variable on the outcome variable while adjusting for no other variable in the model. A. TrueB. FalseC. None of the above 8. The particular method of analysis in a research study depends on: A. Research questionB. Study designC. Level of measurementD. All of the aboveE. A and B only 9. Reviewing relevant literature exposes the researcher to specific designs used by previous researchers. A. TrueB. FalseC. None of the above
One measure of goodness of fit (or the quality) of the estimated regression equation is the… … mean square due to regression … mean square due to error … high correlation between the ‘x’ variables … multiple coefficient of determination ‘R-Squared’
x1 x2 x3 x4 x5 x6 bond width on bond pull strength loop height wire post height observation die height width on length the die the post 8.0 5.2 17.0 28.6 83.0 1.9 1.6 2. 8.0 5.2 19.6 29.6 94.9 2.1 2,3 3 8.3 5.8 19.8 32.4 89.7 2.1 1.8 4. 8.5 6.4 19.6 31.0 96.2 2.0 2.0 8.8 5.8 19.4 32.4 95.6 2.2 2.1 6 9.0 5.2 18.6 28.6 86.5 2.0 1.8 7 9.3 5.6 18.8 30.6 84.5 2.1 2.1 9.3 6.0 20.4 32.4 88.8 2.2 1.9 9.5 5.2 19.0 32.6 85.7 2.1 1.9 10 9.8 5.8 20.8 32.2 93.6 2.3 2.1 11 10.0 6.4 19.9 31.8 86.0 2.1 1.8 12 10.3 б.0 18.0 32.6 87.1 2.0 1.6 13 10.5 6.2 20.6 33.4 93.1 2.1 2.1 14 10.8 6.2 20.2 31.8 83.4 2.2 2.1 15 11.0 6.2 20.2 32.4 94.5 2.1 1.9 16 11.3 5.6 19.2 31.4 83.4 1.9 1.8 17 11.5 6.0 17.0 33.2 85.2 2.1 2.1 18 11.8 5.8 19.8 35.4 84.1 2.0 1.8 19 12.0 6.5 19.4 32.2 88.5 2.1 1.9 20 12.3 5.6 18.8 34.0 86.9 2.1 1.8 21 12.5 18.6 18.6 34.2 83.0 1.9 2.0 22 12.5 18.6 20.8 35.4 96.2 2.3 2.3 From the sample data above, complete the following steps.
The number of entrees purchased in a single order at a Noodles & Company restaurant has had a historical average of 1.65 entrees per order. On a particular Saturday afternoon, a random sample of 22 Noodles orders had a mean number of entrees equal to 1.7 with a standard deviation equal to 0.9. At the 1 percent level of significance, does this sample show that the average number of entrees per order was greater than expected? (a) Choose the correct null and alternative hypotheses. a. Hg: P2 1.65 vs. H: p1.65 c. Hạ: H = 1.65 vs. H: u# 1.65 O a Ob Oc (b-1) Calculate the tstatistic. (Round your answer to 2 decimal places.) tcale (b-2) Find the p-value. (Round your answer to 4 decimal places.) p-value (c) Choose the correct conclusion. O Because the p-value is less than 0.01, we conclude that there is evidence to indicate a significant increase in the average number of entrees per order. O Because the p-value is greater than 0.01, we conclude that there is no evidence to indicate a…
The following information pertains to a simple least squares regression for DEF Corporation: Mean value of the dependent variable 30Mean value of the independent variable 8Coefficient of the independent variable 3Number of observations 12 What is the "a" value for the leasts-quares regression model? a. 60b. 30c. 6d. 0

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