4 (#2) (Short answer: based on material that will be covered in Lecture 17)Suppose that you have a multiple regression model with two explanatory variables. Both arestatistically insignificant in the regression (i.e., you do not reject that either slope is equal to zero at a5% level). Your R-squared value, however, is extremely high (0.95), so you overwhelmingly reject thenull hypothesis that the variables are jointly insignificant (the null would be written Ho: Bi=B2=0). Howcould this be possible?

An important property of R2 is that it is a non-decreasing function of the number of explanatory…

Answered: (#2) (Short answer: based on material…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 22PFA

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XYZ Corporation Stock Prices The following table shows the average stock price, in dollars, of XYZ Corporation in the given month. Month Stock price January 2011 43.71 February 2011 44.22 March 2011 44.44 April 2011 45.17 May 2011 45.97 a. Find the equation of the regression line. Round the regression coefficients to three decimal places. b. Plot the data points and the regression line. c. Explain in practical terms the meaning of the slope of the regression line. d. Based on the trend of the regression line, what do you predict the stock price to be in January 2012? January 2013?
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?
Special Rounding Instructions For this exercise set, round all regression parameters to three decimal places, but round all other answers to two decimal places unless otherwise indicated. Gray Wolves in WisconsinGray wolves were among the first mammals protected under the Endangered Species Act in the 1970s. Wolves recolonized in Wisconsin beginning in 1980.Their population grew reliably after 1985 as follows: Year Wolves Year Wolves 1985 15 1993 40 1986 16 1994 57 1987 18 1995 83 1988 28 1996 99 1989 31 1997 145 1990 34 1998 178 1991 40 1999 197 1992 45 2000 266 a. Explain why an exponential model may be appropriate. b. Are these data exactly exponential? Explain. c. Find an exponential model for these data. d. Plot the data and the exponential model. e. Comment on your graph in part d. Which data points are below or above the number predicted by the exponential model?
Would someone familiar with SPSS be able to help me complete the question please? i) Give the parameter estimates for the regression equations (intercept, slope), R2, and the overall significance of the model (P value) and state whether the regression is significant or not at the α = 0.05 level for each independent variable. ii) Based on your answers to the previous question, is there any evidence that urinary calcium (uca) relates to any of the four other variables? iii) What can you tell about the relative importance of these four variables in determining urinary calcium (uca)?
A researcher is investigating possible explanations for deaths in traffic accidents. He examined data from 2000 for each of the 52 cities randomly selected in the US. The data included information on the followingvariables: Deaths: The number of deaths in traffic accidents per city and Income: The median income per cityAs part of his study, he ran the following simple linear regression model as pictured : Question: Based on the above results, the researcher tested the hypotheses ( Null: B1=0 versus Alternative: B1 not equal to 0) using T test. What do we know about the test statistic of the test, what is the approximate p-value, and value of Rsquared? And based on your result, what is your conclusion? Show your work for full credit.
A large national bank charges local companies for using its services. A bank official reported the results of a regression analysis designed to predict the bank's charges (y), measured in dollars per month, for services rendered to local companies. One independent variable used to predict the service charge to a company is the company's sales revenue (x), measured in Data for 21 companies who use the bank's services were used to fit the model E(y) = β0 + β1x. The results of the simple linear regression are provided below. = 2,700 + 20xInterpret the estimate of β0, the y-intercept of the line. For every $1 million increase in sales revenue, we expect a service charge to increase $2,700. All companies will be charged at least $2,700 by the bank. There is no practical interpretation since a sales revenue of $0 is a nonsensical value. About 95% of the observed service charges fall within $2,700 of the least squares line.
The below data on the production volume x and total cost y (in dollars) for a particular manufacturing operation were used to develop the estimated regression equation y = 1,217.33 + 7.68x. Production Volume Total Cost ($) $ (units) 400 Submit Answer 450 550 600 700 750 4,100 4,900 5,400 5,900 6,500 (a) The company's production schedule shows that 650 units must be produced next month. What is the point estimate of the total cost (in dollars) for next month? (Round your answer to the nearest cent.) $6209.33 7,000 (b) Develop a 99% prediction interval for the total cost (in dollars) for next month. (Round your answers to the nearest cent.) X to $ X (c) If an accounting cost report at the end of next month shows that the actual production cost during the month was $6,700, should managers be concerned about incurring such a high total cost for the month? Discuss. Since $6,700 is within the prediction interval, managers should not be concerned about Incurring such a high total cost for one…
A statistics professor wants to determine how students' final grades are related to mid-term exam scores, applied in the middle of the term, and the number of classes missed. The teacher selects 10 students from his class and obtains the following data as an attachment. Y = b + m1x1 + m2x2 being the general form of the multiple regression equation referring to the data above. Check the alternative that corresponds to the approximate value of b, m1 and m2, respectively: a) 46,39; 0,54; -4,89 b) -4,89; 0,54; 46,39 c) 46,39; -4,89; 0,54 d) -4,89; 46,39; 0,54
The following residual plot has been created as part of the diagnostics of a simple linear regression model relating personal income (y) to monthly spending on clothes (r). (Both variables were measured in euros) [Attached picture] The residual plot indicates a problem. Explain which regression assumption appears to be violated and what can be done to remedy the situation.
When talking about bivariate regression, the regression coefficient will be equal to rxy when: a. the variables are standarized (beta weights, beta) b.variables are unstandarized (b weights) c.when the intercept is equal to 1 d.Never - bivariate regression and correlatin have nothing in common
In testing the validity of a multiple regression model, a large value of the F test statistic indicates that: A. most of the variation in the independent variables is explained by the variation in y B. most of the variation in y is unexplained C. most of the variation in y is explained by the regression equation D. the model provides a poor fit
Recent research into the cost of various medical procedures has shown the impact of certain complications encountered in surgery on the total cost of a patient’s stay in the hospital. The researchers used regression analysis and found the following results: Total cost for patient = Constant, plus a × Length of stay (measured in days), plus b × Presence of one or more complications (= 1 if true, 0 if false), plus c × Use of a laparoscope (= 1 if true, 0 if false) where a, b, c are coefficients of the regression model. The laparoscope is an instrument somewhat like a miniature telescope with a fiber-optic system that brings light into the abdomen. It is about as big around as a fountain pen and twice as long. The following table shows additional regression results presented by the researchers in the study described above. There are two regressions. The right-hand column shows the results for all patients, including those treated with laparoscopic surgery. The left-hand column…

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