Lot Size Regression Problem Nov 20 SOLVED

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y) The results of the regression were: учах+b a-1.25 b-35.644 r2 .541696 r=-0.736 Use this to predict the number of situps a person who watches 10 hours of TV can do (to one decimal place)

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Find the regression​ equation, letting overhead width be the predictor​ (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.1cm. Can the prediction be​ correct? What is wrong with predicting the weight in this​ case? Use a significance level of 0.05. The regression equation is y^=_____+_____x.   overhead width (cm) 8.2 7.4 9.6 7.9 7.6 8.7 weight (kg) 159 156 241 144 154 207   n α=0.05 α = 0.01 4 0.950 0.990 5 0.878 0.959 6 0.811 0.917 7 0.754 0.875 8 0.707 0.834 9 0.666 0.798 10 0.632 0.765 11 0.602 0.735 12 0.576 0.708 13 0.553 0.684 14 0.532 0.661 15 0.514 0.641 16 0.497 0.623 17 0.482 0.606 18 0.468 0.590 19 0.456 0.575 20 0.444 0.561 25 0.396 0.505 30 0.361 0.463 35 0.335 0.430 40 0.312 0.402 45 0.294 0.378 50 0.279 0.361 60 0.254 0.330 70 0.236 0.305 80 0.220 0.286 90 0.207 0.269 100 0.196 0.256 n α = 0.05 α = 0.01

persor Develop an estimated multiple regression equation that relates risk of a stroke to the person's age, systolic blood pressure, and whethet Let xi represent the person's age. Let x2 represent the person's systolic blood pressure. Let represent whether the person is a smoker. If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sgn even d there is a sign before the blank. (Example -300)

Why this type of plot is used in analysing regression models

In the table below ratings data on the quality of the speed of execution and y = overall satisfaction with electronic trades provided the estimated regression equation y = -0.8828 +1.2596x (AAII website). Variation We Sum of Squares (to 4 decimals) Regression Error Total P-value is less than 0.01 can X X X × X Brokerage Scottrade, Inc. Charles Schwab X Fidelity Brokerage Services TD Ameritrade E*Trade Financial Vanguard Brokerage Services USAA Brokerage Services Thinkorswim Wells Fargo Investments Interactive Brokers Speed Satisfaction 3.6 4.0 3.1 3.3 2.7 2.4 3.9 3.8 3.2 3.1 2.6 Zecco.com 3.3 3.6 At the 0.05 level of significance, test whether speed of execution and overall satisfaction are related. Show the ANOVA table. What is your conclusion? F Degrees Mean Source of p-value (to 4 of Freedom (to 4 decimals) decimals) Square (to 2 decimals) 3.4 2.3 2.0 3.4 3.8 3.0 3.0 2.3 3.8 ✔ reject Ho B₁ = 0, we conclude that speed of execution and overall satisfaction are ✔ related.

Data given below are on the adjusted gross income  and the amount of itemized deductions taken by taxpayers. Data were reported in thousands of dollars. With the estimated regression equation , the point estimate of a reasonable level of total itemized deductions for a taxpayer with an adjusted gross income of  is . Click on the datafile to reference the data. Excel File: data14-37.xlsx   Adjusted Gross Reasonable Amount of Itemized Income ($1000s) Deductions ($1000s) 22   9.6   27   9.6   32   10.1   48   11.1   65   13.5   85   17.7   120   25.5     Use the estimated regression coefficients rounded to  decimals in your calculations. а. Develop a  confidence interval for the mean amount of total itemized deductions for all taxpayers with an adjusted gross income of  (to  decimals).

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.

The slope, b represents * O predicted value of Y when X = 0. O the estimated average change in Y per unit change in X. O variation around the line of regression. O the predicted value of Y.

Write a report on your findings. Predictor Constant X_MoneySupply Y_lagIncome S = 2328 Coef 12,718 0.4405 0.7805 R-Sq= 99.4 % Use the report to estimate the model. SE Coef 5392 0.3393 0.1616 T 2.36 1.30 4.83 R-Sq(adj) = 99.4 % P 0.031 0.213 0.000 Y₁ = + x₁ +t-1 Yt (Round the constant to the nearest integer as needed. Round the coefficients to four decimal places as needed.)