### Understanding Predictive Modeling in EducationA professor has discovered that midterm exam scores in her classes can predict final exam scores. This finding employs a regression model as follows: 1. The regression constant (intercept) in the linear prediction rule for forecasting final exam scores from midterm exam scores is 36.2. The regression coefficient (slope) is 0.5.Using this information, we will complete parts (a) through (k) below:#### (a) Indicate the predictor variable.- The predictor variable is: **the midterm exam score.**#### (b) Indicate the criterion variable.- The criterion variable is: [Dropdown Box Not Displayed in the Image].- Click to select your answer(s) and then click Check Answer.> **Note:** A diagram or graph usually accompanies regression analysis to illustrate the linear relationship between the predictor and the criterion variables. However, there is no graphical content provided in this image.This exercise aims to help students understand the practical application of linear regression in educational settings, demonstrating how one key academic measure can effectively predict another. This kind of analysis can be beneficial not only in academic assessments but also in broader educational research and student performance analytics.---In this breakdown, the details are clearly structured to align with educational content. Definitions, steps, and goals are highlighted for educational clarity, which is essential for a learning environment.

Introduction: The variable in a regression model that helps to estimate or predict the values of…

A professor has found that scores on the midterm exam in her classes predict scores on the final exam. The regression constant in the linear prediction rule for predicting final exam scores from midterm exam scores is 36 and the regression coefficient is 0.5. Complete parts (a) through (k) below. (a) Indicate the predictor variable. The predictor variable is the midterm exam score. (b) Indicate the criterion variable. The criterion variable is

A professor has found that scores on the midterm exam in her classes predict scores on the final exam. The regression constant in the linear prediction rule for predicting final exam scores from midterm exam scores is 36 and the regression coefficient is 0.5. Complete parts (a) through (k) below. (a) Indicate the predictor variable. The predictor variable is the midterm exam score. (b) Indicate the criterion variable. The criterion variable is

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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We expect a car's highway gas mileage to be related to its city gas mileage (in miles per gallon, mpg). Data for all 1259 vehicles in the government's 2019 Fuel Economy Guide give the regression line highway mpg = 8.720 + (0.914x city mpg) for predicting highway mileage from city mileage. 1 O Macmillan Learning (b) What is the intercept? Give your answer to three decimal places. intercept: Why is the value of the intercept not statistically meaningful? The value of the intercept is an average value calculated from a sample. The value of the intercept represents the predicted highway mileage for city gas mileage of 0 mpg, and such a prediction would be invalid since 0 is outside the range of the data. The value of the intercept represents the predicted highway mileage for slope 0. O The value of the intercept represents the predicted city mileage for highway gas mileage of 0 mpg, and such a car does not exist. mpg
Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS df 208.008457 314.295372 37 2 104.004228 8.49446952 MS 522.303829 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared Root MSE = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What is the predicted height for a child born to a mother…
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 43 inches. Is the result close to the actual weight of 237 pounds? Use a significance level of 0.05. Chest_size_(inches) Weight_ (pounds)50 27954 33944 21954 29240 20136 116 Is the result close to the actual weight of 237 pounds? A. This result is close to the actual weight of the bear. B. This result is very close to the actual weight of the bear. C. This result is not very close to the actual weight of the bear. D. This result is exactly the same as the actual weight of the bear.
The data show The chest size and weight of several bears find regression equation, letting chest size be the independent variable then find the best predicted weight of a bear with a chest size of 51 inches is the result close to the actual weight of 422 pounds use a significance level of 0.05 Chest size : 45, 50, 43, 43, 52, 52. Weight : 352, 374, 275, 314, 440, 367.
A financial website reported the beta value for a certain company was 0.86. Betas for individual stocks are determined by simple linear regression. The dependent variable is the total return for the stock, and the independent variable is the total return for the stock market, such as the return of a market index. The slope of this regression equation is referred to as the stock's beta. Many financial analysts prefer to measure the risk of a stock by computing the stock's beta value. Suppose the following data show the monthly percentage returns for the market index and the company for a recent year. Month Market Index% Return Company% Return August -3 4 September 8 7 October 0 1 November -2 1 December -5 0 January 0 0 February 7 7 March 0 -2 April 2 0 May -5 -1 a. Develop the least squares estimated regression equation. (Let x = Market Index % Return (as a %), and let y = Company % Return (as a %). Round your numerical values to four decimal places.)
The President of the Farmers Association wants to know how the amount of fertilizer and theamount of water given to plants affect their growth. The results were inputted into MINITABso as to fit the model.Regression Analysis: Growth versus Water, FertilizerThe regression equation isGrowth = ? + ? Water + ?FertilizerPredictor Coef SE Coef T PConstant 42.085 ** 3.118 0.009Water 0.178 0.386 0.460 0.654Fertilizer 5.790 0.952 6.083 *S = 6.159 R-Sq = 77.1%% R-Sq(adj) = 73.3%Analysis of VarianceSource DF SS MS F PRegression 2 1532.603 766.301 *** ****Residual Error 12 455.130 37.928Total 14 1987.733iv. Conduct a hypothesis test, at the 5% level of significance, to determine whether ? issignificant. [4]v. What would be the growth of the plant if 4g of fertilizer and 7g of ater was given to it
Suppose you use regression topredict the height of a womanscurrent boyfriend by using her ownheight as the explanatory variable.Height was measured in feet from asample of 100 womenundergraduates, and their boyfriends,at Dalhousie University. Now, supposethat the height of both the womenand the men are converted tocentimeters. The impact of thisconversion on the slope is:
How would a researcher know whether linear regression was the appropriate statistical technique to use? What are some of the benefits of fitting the relationship between two variables to an equation for a straight line? (Don't hand writing solution)