1.It has been noted that there is a positive correlationbetween the U.S. economy and the height of women'shemlines (distance from the floor of the bottom of askirt or dress) with shorter skirts corresponding toeconomic growth and lower hemlines to periods ofeconomic recession. Comment on the conclusion thateconomic factors cause hemlines to rise and fall.# of Absences (in weeks) Exam Grade1234523 0149085959280

Given that: # of Absences (in weeks), X Exam Grade, Y 2 90 3 85 0 95 1 92 4 80

Answered: 1. It has been noted that there is a…

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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Below is a table showing the relationship between age and bone density of a typical healthy adult male. Age (years) 25 35 45 55 65 75 85 Bone density (mg/cm²) 1165 1135 1085 1060 1015 975 910 Using your line of best fit find the equation for this line. Describe the correlation between the two variables and what conclusions can you make from this set of data? Using graph plotted extrapolate to find the bone density of a 90 years old typical adult.
The linear correlation between violent crime rate and percentage of the population that has a cell phone is −0.918 for years since 1995. Do you believe that increasing the percentage of the population that has a cell phone will decrease the violent crime rate? What might be a lurking variable between percentage of the population with a cell phone and violent crime rate? Question content area bottom Part 1 Will increasing the percentage of the population that has a cell phone decrease the violent crime? Part 2 What might be a lurking variable between percentage of the population with a cell phone and violent crime rate?
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Price/Book Value Ratio Return on Equity 13.032 1.405 8.305 2.113 6.654 1.239 3.262 2.449 5.291 2.398 7.719 0.353 2.569 7.593 5.104 2.012 4.797 2.182 4.129 1.918 1.549 1.951 5.046 2.417 2.159 3.011 1.725 5.582 4.698 Growth% 6.385 11.846 135.669 12.459 0.073 25.092 14.188 8.804 22.766 38.082 18.985 25.696 24.519 19.666 11.624 22.849 49.965 69.649 36.696 3.819 41.139 9.218 29.108 17.772 25.114 29.295 23.764 31.405 9.497 14.759 18.541 12.026 39.016 14.228 39.439 14.097 27.022 14.841 13.237 20.669 17.311 14.887 15.849 5.601 16.775 11.172 8.401 16.161 18.404 23.973 16.673 14.725 46.605 28.839 52.021
The consumption function captures one of the key relationships in economics. It expresses consumption as a function of disposal income, where disposable income is income after taxes. The attached file "Regression-Dataset1" shows data of average US annual consumption (in $) and disposable income (in $) for the years 2000 to 2016. a)what is the Pearson correlation value between income and consumption? Select one: Ⓒa. 0.978 b. 0.109 c. None of the above d. 0.791
2. State whether the variables described have a positive correlation, a negative correlation, or no correlation. (a) Income and SAT scores (b) Length of a person's hair and how far they sit from the screen in a movie theater. (c) Hours of exercise per week and blood pressure.
An economist wants to determine the relation between one’s FICO score, ?, and the interest rate (in percent) of a 36-month auto loan, ?. Credit score,x Interest Rate(percent), y 545 18.982 595 17.976 640 12.218 675 8.612 705 6.680 750 5.150 (a) What is the best fit line that could be used to predict the interest rate? (b) Interpret the slope and vertical intercept. (c) How would you determine if your best fit line is a good fit for this data? Explain using at least two methods
Help me fast so that I will give Upvote.
Prices and mileage of 48 Dodge 2500 Pickup trucks with Cummins diesel engines. Prices ranged from $28,925 to $68,650. Mileage ranged from 315 to 186,466. While I did not share the trucks were 2013 to 2020 models. Please show all work. Interpret b0 in the context of this problem. Interpret b1 in the context of this problem. What percent of total variation in prices is explained by the regression model? Provide evidence to support your answer. Price Mileage 52293 34450 50987 23372 62988 21738 37500 81176 66000 23118 47601 71143 47750 18214 42000 32303 50987 23372 28925 143737 42050 58561 51738 9691 51500 23969 44988 48781 29500 114809 46975 16918 63999 15507 46975 16918 43222 50459 38386 87192 42000 68722 62250 14042 62998 21738 32130 103644 52293 34450 52900 68883 53172 3656 53494 315 39594 88441 63246 23631 58250 34974 59391 18383 50650 42737 54976 84200 68650 14401 51114 47433 43900 74106 37450…
2. Which of the following statements is false? Explain your answer. A. A price index can be used to compare changes in prices over time. B. A graph of the average monthly price of gas is an example of an index. C. Price index values are often calculated relative to a base value at a certain point in time or in a certain place. D. Index values can be greater than 100.
A negative correlation indicates that as the values of one variable increase, the values of a second variable decrease. O True O False
2. The table below gives the sales, S, of Intel Corporation, a leading manufacturer of integrated circuits. 2003 30,100 2004 34,200 2006 2007 2008 Year S (millions of $) 2005 38,800 35,400 38,300 37,600 a. Find the change in sales between 2005 and 2006. b. Find and interpret the average rate of change in sales between 2005 and 2006.
Explain trend projection with regression?

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