Week 13 DB – Simple Linear Regression Here we discuss prediction using one variable - simple linear regression. This will be a detailed discussion worth 10 points so plan accordingly. Please use the...

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Week 13 DB – Simple Linear Regression Here we discuss prediction using one variable - simple linear regression. This will be a detailed discussion worth 10 points so plan accordingly.  Please use the following Instructions to write your reply. 1) Explain what regression gives us that correlation does not and explain the relationship between the correlation coefficient and the coefficient of determination. 2) If the regression is our measure of the center (the central tendency among a set of x,y data value pairs), what is the measure of spread and how is that calculated? 3) How could you use regression analysis to predict something of interest in your personal life or work?  If you did this, how would you explain it to friends or colleagues? 4) What are some of the common errors or problems we need to check for before buying the results of a regression analysis and how would you avoid those types of errors?  Be sure to mention how you would check your data to see if it meets the assumptions of using regression and then write the rest of your answer.  Write this essentially as a note of warning to yourself for what to watch out for when performing regression analysis and what to watch out for when reading published regression results. ** The main post should be at least 400 words.  Please also reply to the next post in 75 words. 1) Explain what regression gives us that correlation does not and explain the relationship between the correlation  coefficient  and the coefficient  of determination.   Correlation shows the relationship between the two variables, while regression allows us to see how one affects the other. The data shown with regression  establishes  a cause and effect, when one changes, so does the other, and not always in the same direction. With correlation, the variables move together  ( Calvello,2020).  The correlation  coefficient   direction and strength  value  are   represented  by “r” ,  it calculates the area covered by the x and y factors. The closer r is to ±1.0, the stronger the correlation, and the more closely two factors are  related (Privitera, 2018 ). The   coefficient  of determination  is the squared correlation  coefficient  (r 2 ) .  We use the  coefficient  of determination to show the lack of  consistency  of a factor in relation to another factor.   2) If the regression is our measure of the center (the central tendency among a set of  x,y  data value pairs), what is the measure of spread and how is that calculated?   The measure of spread  is  important, because  it shows the relationship with measures of central tendency. Its shows how the mean of the data  could  represent  the data .  Measures of spread include  the range, quartiles and the interquartile range,  variance  and standard deviation  (A.B.S., 2021).   3) How could you use regression analysis to predict something of interest in your personal life or work ?   If you did this, how would you explain it to friends or colleagues?   An example to explain to friend or colleagues about regression analysis  to predict something would be weigh gain. If I were gaining weight at a steady rate  for several months  in order to  prepare for competition. I could then make a prediction of how much I will weigh just in time for the competition.   4) What are some of the common errors or problems we need to check for before buying the results of a regression analysis and how would you avoid those types of errors ?   Be sure to mention how you would check your data to see if it meets the assumptions of using regression and then write the rest of your answer .   Write this  essentially as  a note of warning to yourself for what to watch out for  when performing regression analysis and what to watch out for when reading published regression results.   Some of the common errors or problems we need to check for before buying the results of a regression analysis is  knowing that not all data will create a linear line and that there is sometime nonlinear data. To ensure this error is not made I would have  to ensure that my variables have linear relations and calculate the correlation coefficients correctly.  When creating mock  data,  I should also make sure that my independent variables have a linear relationship and correlate.  I also need to remember that correlation does not also mean  causation for my study.  To avoid  making  errors I should make sure that  I can  visually  see my results through graphs, calculate my confidence level and know my p-value in relations to my confidence level.  When using mock data or analysis real data I should also make sure I have a thorough understanding of what my variables are and how they could  possibly relate  and not just assume  because of what I think to be general knowledge.