The following results were obtained in the amalysis of data on yield of dry barkIn ounces Y and in vears X of 200 cinchona plants.ageYAverage9.216.5Standard Deviation0i - A. mi 2.1Y 4.2and correlation coefficient •84Fnd the two lines of regression and estimate the yield of dry bark of a plantof age 8 years.

Answered: Image /qna-images/answer/56cd9d2b-44a2-4221-938f-bd2fcf03e788.jpg

Answered: merollowing results were obtained in…

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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9:33 AA www-awn.aleks.com O REGRESSION AND CORRELATION Predictions from the least-. Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximum temperature (denoted by x, in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Temperature, x (in degrees Coffee sales, y (in dollars) Fahrenheit) 68.3 1787.6 38.9 2311.0 72.9 1684.0 2200 58.8 1979.5 63.4 1816.4 2000 75.8 1997.6 46.9 2131.7 71.0 1973.9 100 38.7 2018.2 1400 53.9 1597.5 45.9 1996.1 75.4 1501.0 50.9 2266.3 81.7 1574.5 Figure 1 45.5 1798.9 Send data to Excel After deciding on the appropriateness of a linear model relating coffee sales and maximum temperature, the managers…
PLEASE I NEED HELP WITH THIS. (Please dont make it longer than 30 minutes I really need help.) :) please. With this research: The average temperature of Hamilton in January 2020 was less than 32°F (0°C). Weather in Hamilton in January 2020 (Ontario) - Detailed Weather Forecast for a Month (world-weather.info) You will need to calculate, explain,: B)Draw a graphic A)Calculates central trend measurements and data dispersion measurements. B)Draws the best adjusted line from a point cloud and indicates the type of correlation.
Simple linear regression results: Dependent Variable: Heart. DiseaseIndependent Variable: BikingHeart. Disease = 18.115809 - 0.20845321 BikingSample size: 68R (correlation coefficient) = -0.94616452R-sq = 0.8952273Estimate of error standard deviation: 1.5273175 Correlation between Biking and Heart. Disease is: -0.94616452. Correlation between Smoking and Heart. Disease is: 0.32421. 1) State r 2 (i.e., the coefficient of determination) for “Biking” and “Heart.Disease” and explain what this value means in context of the data set.
mean of x = 2.882, standard deviation = 1.634 Mean of y = 2.588, standard deviation = 0.246 The correlation coefficient is 0.159 slope coefficient for regression line = .0239 Find the intercept for the regression line.
Waterbury Insurance Company wants to study the relationship between the amount of fire damage and the distance between the burning house and the neorest fire station. This information will be used in setting rates for insurance coverage. For o somple of 30 claims for the last year, the director of the actuarial department determined the distance from the fire station (x) and the amount of fire domage, in thousands of dollars (y). ANOVA table Source Regression Residual SS df MS F 1,870.5782 1,870.5782 41.39 1,265.4934 3,136.0716 28 45.1962 Total 29 Regression output Standard Variables Coefficients Error t(df-28) Intercept Distance-X 13.76815 3.106 2.914 3.77es e. 5861 6.43 Click here for the Excel Data File a-1. Determine the regression equation. (Round your answers to 3 decimal places.) y3D X. a-2 Is there a direct or indirect relotionship between the distance from the fire station and the amount of fire damage? The relationship between distance and damage is b. How much domage would…
A study was done using a treatment group and a placebo group. The results are shown in the table. Assume that the two samples are independent simple random samples selected from normally distributed populations, and do not assume that the population standard deviations are equal. Complete parts (a) and (b) below. Use a 0.05 significance level for both parts. Treatment Placebo μ μ1 μ2 n 27 39 x 2.38 2.65 s 0.87 0.61 a. Test the claim that the two samples are from populations with the same mean. What are the null and alternative hypotheses? A. H0: μ1≠μ2 H1: μ1<μ2 B. H0: μ1<μ2 H1: μ1≥μ2 C. H0: μ1=μ2 H1: μ1>μ2 D. H0: μ1=μ2 H1: μ1≠μ2 Your answer is correct. The test statistic, t, is (Round to two decimal places as needed.)
The regional manager of a franchise business is interested in understanding how income in a region affects sales. Below is a regression output for sales ($’000) regressed on the average household income of an area ($’000) Linear Fit Sales = 14.5774 + 2.9048*Income Summary of Fit RSquare 0.9683 RSquare Adj 0.9630 Root Mean Square Error 3.1083 Mean of Response 43.6250 Analysis of Variance Source DF Sum of Squares Mean Square F Ratio Model 1 1771.9048 1771.90 183.3946 Error 6 57.9702 9.66 Pro>F C. Total 7 1829.8750 ItI Intercept 14.5774 2.4101 6.05 0.0009* Income 2.9098 0.2145 13.54 < 0.0001* Answer the following questions: (i) What is the average sales across all regions? (ii) Interpret the slope of regression (iii) What is the prediction of the value of sales in a region with an average…
According to recent UN figures, the annual gun homicide rate is 62.4 per one mil- lion residents in the US and 1.3 per one million residents in Britain. Compare the proportion of residents killed annually by guns using the (a) difference of propor- tions, (b) relative risk. Which measure is more useful for describing the strength of association? Why?
The amounts of 6 restaurant bills and the corresponding amounts of the tips are given in the below. Assume that bill amount is the explanatory variable and tip amount the response variable. Bill 32.98 49.72 52.44 64.30 97.34 88.01 Tip 4.50 5.28 7.00 7.70 16.00 10.00 (a) Find the correlation: r= (c) The regression equation is y^= . (d) If the amount of the bill is $90, the best prediction for the amount of the tip is $ .Note: Enter your answer as a number xx.xx (e) According to the regression equation, for every $5 increase in the bill, the tip should INCREASE or DECREASE) by $ .
The attached results are for a multiple regression study of smartphone addiction (SSA-SV) proneness in relation to 1) Gender 2)Age 3) Anxiety (GAD-7). I just want to clarify that age is statistically significant with p = 0.006; is this correct? What's throwing me off is the notation "< .001" on the top line under the p category. I was under the impression that the p-value would need to be less than 0.001.
I need interpret Histogram and P-P plot graphs using with tables. Used dependent variable Y1, and other three independant variables X1, X2 and X3. Please collect the attachements
What does the slope and the intercept mean in this setting?

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