Please complete the regression table shown in the picture below 234CP89AveScore171312IS16141616181915.6xNo. ofDStudy Hows X-X9473598093856679ㅋㅋ9159.7yy-|(x-x)(y-7)(x-7)²2 (y-9) ²у-у120/00So06040g-intercept,20a oLinear Regression function5атыхySlope (2) of RegressionsSyb=r-Sx4-interapt (a) of regression lineⒸ- @j j -b x(0(0)toIs26Pearson Correlation coefficient (r)~ = [ ((x-x)(y-4₁ ) )Syslove{(x-x)² Ely-91²[(y-g)²(x-x)²

The data of score and number of study hours are given. Sr. No. score(x) No of study hour(y) 1…

Answered: dy Have x-X y-y (x-x)(y-7)(x-x)² 94 7.3…

dy Have x-X y-y (x-x)(y-7)(x-x)² 94 7.3 59 80 73 85 66 | (y-9) ² S 60 g-interupt 2 a D 5 Linear Regression function y = a + bx Slope (6) of Rega Sy Sv C

Trigonometry (MindTap Course List)

8th Edition

ISBN:9781305652224

Author:Charles P. McKeague, Mark D. Turner

Publisher:Charles P. McKeague, Mark D. Turner

Chapter4: Graphing And Inverse Functions

Section: Chapter Questions

Problem 6GP: If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use...

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If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?
The following table provides values of the function f(x,y). However, because of potential; errors in measurement, the functional values may be slightly inaccurately. Using the statistical package included with a graphical calculator or spreadsheet and critical thinking skills, find the function f(x,y)=a+bx+cy that best estimate the table where a, b and c are integers. Hint: Do a linear regression on each column with the value of y fixed and then use these four regression equations to determine the coefficient c. x y 0 1 2 3 0 4.02 7.04 9.98 13.00 1 6.01 9.06 11.98 14.96 2 7.99 10.95 14.02 17.09 3 9.99 13.01 16.01 19.02
Respiratory Rate Researchers have found that the 95 th percentile the value at which 95% of the data are at or below for respiratory rates in breath per minute during the first 3 years of infancy are given by y=101.82411-0.0125995x+0.00013401x2 for awake infants and y=101.72858-0.0139928x+0.00017646x2 for sleeping infants, where x is the age in months. Source: Pediatrics. a. What is the domain for each function? b. For each respiratory rate, is the rate decreasing or increasing over the first 3 years of life? Hint: Is the graph of the quadratic in the exponent opening upward or downward? Where is the vertex? c. Verify your answer to part b using a graphing calculator. d. For a 1- year-old infant in the 95 th percentile, how much higher is the walking respiratory rate then the sleeping respiratory rate? e. f.
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?
The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature (x1), the number of days in the month (x2), the average product purity (x3), and the tons of product produced (x4). A multiple linear regression analysis was applied to the experimental data using MINTAB and the following results were obtained: Regression Analysis: y versus x1, x2, x3, x4 Regression Equation y = -123 + 0.757 x1 + 7.52 x2 + 2.48 x3 - 0.481 x4 Coefficients Term Coef SE Coef T-Value P-Value VIF Constant -123 157 -0.78 0.459 x1 0.757 0.279 2.71 0.030 2.32 x2 7.52 4.01 1.87 0.103 2.16 x3 2.48 1.81 1.37 0.212 1.34 x4 -0.481 0.555 -0.87 0.415 1.01 Analysis of Variance Source DF Seq SS Seq MS F-Value P-Value Regression 4 5600.5 1400.1 10.08 0.005 Error 7 972.5 138.9 Total 11 6572.9 Then, the upper limit of the two-sided 95% confidence interval on the slope B2 is equal to а. 17 O b. 20 19
APP Internet users (millions) Year 1994 1997 2000 2003 2006 2009 2012 Users (1) 2.6 17.6 73,3 170,7 368,5 594.7 871,4 #04161 let & represent the number of internet users since 1994. regression Find an exponenial model (y=ab^) for the data set. 2) Find the number of users in 2022.
In (WAGE) = B, + B,EDUC + B,EDUC² + B,EXPER+ B,EXPER² + Bo (EDUC × EXPER) + e The above regression is fitted with a range of restrictions. The regression results are given below: Coefficient Estimates and (Standard Errors) Eqn (A) Eqn (C) Eqn (D) Eqn (E) Variable Eqn (B) 0.403 1.483 1.812 2.674 1.256 (0.771) (0.495) (0.494) (0.109) (0.191) EDUC 0.175 0.0657 0.0669 0.0997 (0.091) (0.0692) (0.0696) (0.0117) EDUC -0.0012 0.0012 0.0010 (0.0027) (0.0024) (0.0024) EXPER 0.0496 0.0228 0.0314 0.0222 (0.0172) (0.0091) -0.00038 -0.00032 (0.0104) (0.0090) EXPER -0.00060 -0.00031 (0.00019) (0.00019) (0.00022) (0.00019) EXPER X EDUC -0.001703 (0.000935) SSE 37.326 37.964 40.700 52.171 38.012 What is the value of F-test statistic for the restriction on the coefficients of Equation E that give Equation D? Note: Sample size N is 200 Select one: O a. 3.89 O b. 5.85 O. 1.78 O d. 73.01
The United States gross national product, in trillions of dollars, is given in the table below. Date Gross national product 15.0 2010 2011 15.5 16.2 |2012 | 2013 16.7 (a) Find the equation of the regression line. (Let t be the number of years since 2010. Round regression line parameters to two decimal places.) Gt) = Explain the me aning of its slope. The slope of the line tells us that each year the gross national product increases by trillion dollars. (b) Plot the data points and the regression line. G 17 17 16 16 15 15 14 14 13 13 12 12 1 2 3 1 2 3 G G 17 17 16 16 15 15 14 14 13- 13 12 12 1 2 3 1 2 3 (c) When would you predict that a gross national product of 17.3 trillion dollars would be reached? (Round your answer to two decimal places.) The actual gross national product in 2014 was 17.3 trillion dollars. What does that say about your prediction? • This prediction is accurate. O This prediction is not accurate.
when the regression line passes through the origin then
Let x be the size of a house (sq ft) and y be the amount of natural gas used (therms) during a specified period. Suppose that for a particular community, x and y are related according to the simple linear regression model with the following values. ? = slope of population regression line = 0.014? = y intercept of population regression line = -4 (a) What is the equation of the population regression line?y = (b) What is the mean value of gas usage for houses with 2100 sq ft of space?(c) What is the average change in usage associated with a 1 square foot increase in size?(d) What is the average change in usage associated with a 100 square feet increase in size?
A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client (measured in pounds), Two variables thought to affect weight loss are client's length of time on the weight-loss program and time of session These variables are described below: Y-BO+B1'X+82'D 83'X'D+E Y-Weight loss (in pounds) X- Length of time in weight-loss program (in months) D-1 if morning session. O if not in terms of the Bs in the model, what is the difference between the weight loss of an individual who has spent 3 months in the program when attending the morning session, and an individual who has spent 2 months in the program when attending the evening session? OB1+83 OB1+82-83 OB1+82+283 O81+82+383
The least squares regression line for a set of data is calculated to be y = 24.8 + 3.41x. (a) One of the points in the data set is (4, 37). Calculate the predicted value. (b) For the point in part (a), calculate the residual.