please solve parts d, e and f in red color bracket fertilizer dose X on crop yield Y. Sample data were as follows:3Fertilizer 2dose (X)Cropyield (Y)NOTE:{X=27 ΣΧ=87 ,ΣΥ=153 , ΣΥ=3333, ΣΧΥ=522Give( Find)the least squares regression line Y=Bo^+ B₁^X, and giveo²^ and R²Note: [(Y₁-Y^)²=70.5and (X; -Xbar)²=6 .Computepoint estimate for the correlation coefficient r=p^?C.Test at a=0.05 the null hypothesis Ho: No fertilizer effect on crop yield?d.Give 95% Confidence interval for the population slope B₁of the regression line?e.Give 95% Confidence Interval (CI) for the population mean E(Y|X=4)?Give 95% CI for the population mean difference E(Y|X=4)- E(Y|X=2)?g. Regard fertilizer as qualitativefactor( i.e. disregard factor X is quantitative),give(Find)ONE_WAY ANOVA for this problem, and giveo and R²?Note: (Y₁-Ybar)²-732and(n-1) S² = 30 for i=1,2,3утеTest (at a=0.05) for no fertilizer effect on crop yield using the one-way ANOVAapproach of part (g)?i.Giveusing ANOVA of part (g) 95% Confidence interval for the differencebetweenthe mean crop yield at fertilizer doze X=4 and the mean crop yield atfertilizer doze X=2?Note :o^-30/6=54"An experiment was conducted to study the effect of232831820322424426428

Given the data asFertilizer dose (X)Crop yield (Y)242328318320322424426428

Answered: fertilizer dose X on crop yield Y.…

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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A group of 13 healthy children and adolescents participated in a phycological study designed to analyze the relationship between age and average total sleep time (ATST). To obtain a measure for ATST (in minutes), recordings were taken on each subject on three consecutive nights and then averaged. Results are provided to you in Sleep&Age.xlsx Download Sleep&Age.xlsx file. (2 points) Determine the least-squares regression line for predicting average total sleep time using age. (2 points) Make a scatter plot of the data with ATST on the y-axis (vertical axis) and Age on the x-axis (horizontal axis) with least squares regression line overlaid on the top (i.e.: obtain the fitted line plot). Make sure to attach the plot below. (7 points) Check the assumptions for the simple linear regression. Attach any plots you used check the assumptions and comment on them. (7 points) We want to see if the average sleep time decreases as the children grow older. Write the appropriate null and…
Hi, can you please work on question (b). Finding the least square regression line.
Suppose that the regression model is defined as: y=B1x+u Use the least squares method to estimate the slope of the regression line. Define the objective function that will be used in the estimation. Lastly, enumerate the steps in deriving the estimator of B1
Draw a scatterplot of the right foot temperature (y) versus the left foot temperature (x). Then draw the least-squares regression line on the graph.
An owner of a home in the Midwest installed solar panels to reduce heating costs. After installing the solar panels, he measured the amount of natural gas used y (in cubic feet) to heat the home and outside temperature x (in degree-days, where a day's degree-days are the number of degrees its average temperature falls below 65° F) over a 23-month period. He then computed the least-squares regression line for predicting y from x and found it to be ŷ = 85 + 16x. The software used to compute the least-squares regression line for the equation above says that r2 = 0.98. This suggests which of the following? 1. Gas used increases by square root of 0.98 = 0.99 cubic feet for each additional degree-day? 2. Although degree-days and gas used are correlated, degree-days do not predict gas used very accurately. 3. Prediction of gas used from degree-days will be quite accurate.
Discuss minimum of two (2) Ordinary least square (OLS) assumptions and their importance for a validity of regression model
A researcher at a large company has collected data on the beginning salary and current salary of 50 randomly selected employees. The correlation between the data sets is r = 0.912. The summary statistics from the data collected are shown bellow: Beginning Salary Ending Salary Mean x¯=56,340 y¯=82,070 Standard deviation Sx = 5,470 Sy = 7,800 Use a complete sentence to describe the strength and direction of the linear relationship between beginning salary and current salary. Find the equation of the least-squares regression line. (Round to two decimal places)
(a) Compute the least-squares regression line for predicting U.S, emissions from non-U.S. emissions. Round the slope and y-intercept values to four decimal places.
A researcher at a large company has collected data on the beginning salary and current salary of 48 randomly selected employees. The least-squares regression equation for predicting their current salary from theirbeginning salary isY = -2500 + 2.1X Where Y is the current salary, and X is the beginning salary.Jay started working for the company earning $ 30,000. He currently earns $60,000. What is the residual for Jay (assuming no extrapolation error)? Please show calculations
In an attempt to develop a model of wine quality as judged by wine experts, data was collected from 50 red wine variants of a certain type of wine. A multiple linear regression model to predict wine quality, measured on a scale from 0 (very bad) to 10 (excellent) was developed, based on alcohol content (%), X1, and the amount of chlorides, X2. Perform a multiple regression analysis and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Determine the VIF for each independent variable in the model, with VIF1 and VIF2 being the VIF for X1 and X2, respectively.
A study was conducted to assess the relationship between students’s score in final exam (y) and number of hours spent for exam (x) in each day. Data on a random sample 20 students were obtained and a regression model was estimated; and the least squares estimates obtained are: intercept a=28.5 and slope b=4.3 with SE(b)=Sb=0.017. The SS are: TSS=2540 and ESS=850. ****** QA) What is the difference between exam score obtained by two students one who studied 5 hours and the other who studied 9 hours per day. QB) In the above Question 1, find 95% CI for the slope and interpret it. In the above Question 1, find and interpret the coefficient of determination (r-square value).
Suppose we are given a least squares regression line ˆy=43x+10 obtained from data where the range of data for the explanatory variable ranges from a minimum value of 3 to maximum value of 40. If we were to find the predicted value at x= 56, we would be from the data. Please select one of the following answers (i) predicting (ii) conditioning (iii) extrapolating (iv) estimating

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