Example 12.12 A company has reorganised its sales department. The following data show its weeklysales both before and after reorganisation. The period for comparison is taken from January to Marchin two successive years.Week No.1234567 8 91015 13 11 17111819Sales prior to reorganisation (Rs lakh) 12Sales after reorganisation (Rs lakh) 1615 1013 15 14 1211 172217 14Can it be concluded that the reorganisation of the sales department of the company has resulted in asignificant increase in its sales?

Answered: Image /qna-images/answer/a0e03b90-5420-40e7-bdcd-57ba07a69cb2.jpg

Answered: Example 12.12 A company has reorganised…

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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6. The Motion Picture Association of America reports a drop-off of video disc sales. The data in the table below shows consumer spending on video discs (DVD, Blue-ray, and UltraHD Blu-ray) in the United States in the years 2015-2018. Year Consumer Spending on video discs (in billions of dollars) 2015 9.1 2016 8.0 2017 6.8 2018 5.8 (a) What was the average rate of change in consumer spending from 2015 to 2018? (Be sure to include units.) (b) Use the average rate of change calculated above to predict consumer spending on video discs in 2020?
The table below represents the monthly unemployment rates in the US from January of 2005 through May of 2016. Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2005 5.3% 5.4% 5.2% 5.2% 5.1% 5.0% 5.0% 4.9% 5.0% 5.0% 5.0% 4.9% 2006 4.7% 4.8% 4.7% 4.7% 4.6% 4.6% 4.7% 4.7% 4.5% 4.4% 4.5% 4.4% 2007 4.6% 4.5% 4.4% 4.5% 4.4% 4.6% 4.7% 4.6% 4.7% 4.7% 4.7% 5.0% 2008 5.0% 4.9% 5.1% 5.0% 5.4% 5.6% 5.8% 6.1% 6.1% 6.5% 6.8% 7.3% 2009 7.8% 8.3% 8.7% 9.0% 9.4% 9.5% 9.5% 9.6% 9.8% 10.0% 9.9% 9.9% 2010 9.7% 9.8% 9.9% 9.9% 9.6% 9.4% 9.5% 9.5% 9.5% 9.5% 9.8% 9.4% 2011 9.1% 9.0% 9.0% 9.1% 9.0% 9.1% 9.0% 9.0% 9.0% 8.8% 8.6% 8.5% 2012 8.2% 8.3% 8.2% 8.2% 8.2% 8.2% 8.2% 8.1% 7.8% 7.8% 7.8% 7.9% 2013 7.9% 7.7% 7.5% 7.5% 7.5% 7.5% 7.3% 7.2%…
Data for a particular country on consumer debt (as a percentage of personal income) and mortgage debt (also as a percentage of personal income) for the 10 years from 2006 to 2015 are shown in the following table. Consumer Debt r = 6.72 7.07 6.97 6.69 6.19 5.58 5.15 4.95 4.74 4.59 Mortgage Debt 5.96 5.97 5.88 5.60 5.17 5.06 4.96 5.22 5.30 5.45 (a) What is the value of the correlation coefficient for this data set? (Round your answer to four decimal places.)
47. Given the following data : Weekly take-home pay (wages) Consumer price index Year 109.50 112.8 2012 2013 112.20 118.2 2014 116.40 127.4 2015 125.08 138.2 2016 135.40 143.5 2017 138.10 149.8 () What was the real average weekly wage for each year ? (i) In which year did the employees have the greatest buying power ? (ii) What percentage increase in the weekly wages for the year 2017 is required (if any) to provide the same buying power that the employees enjoyed in the year in which they had the highest real wages ?
For the past 10 years, you have been observing the sales of your company since you embarked on an aggressive advertising campaign. You have been recording the amounts spent on advertising and the corresponding sales as follows: Year Advert (X) Sales (Y) 2001 10 44 2002 9 40 2003 11 42 2004 12 46 2005 11 48 2006 12 52 2007 13 54 2008 13 58 2009 14 56 2010 15 60 You would like to determine whether a relationship exists between your two variables of interest and therefore decide to run a regression. Specify the estimation model you will use (the econometric form). Identify the independent and the dependent variables. Use an econometric package of your choice (EViews, SPSS, STATA etc) to estimate the model. From the regression output, report the coefficients, standard errors, t-statistics, probability and R-squared (report the results in a table). Re-write the specified model in (a) with values…
Example 23. Draw less than and more than ogive for the data given biow. No. of Companies Profits No. of Companies Profits (Rs. Lakhs) (Rs. of Lakhs) 10-20 6. 60-70 16 20-30 8. 70-80 8 30-40 12 80-90 40-50 18 90-100 50-60 25
Susan, a personal trainer, was interested in whether or not there was a linear relationship between the number of visits her clients made to the gym each week and the average amount of time her clients exercised per visit. She took the following data. Client 1 2 3 4 5 6 Number of visits per week 1 3 4 2 3 5 Average time spent exercising per visit (hours) 2 1.5 1 2 2 0.30 Using the best fit line, estimate the average time spent exercising per visit for 4 visits per week. Group of answer choices 1.03 hours 1 hour 10.3 hours 2 hours

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