I need assistance in completing algebra project that is due on Sunday

I will be positing screen shots of everything that is needed to complete this assignment ..this assignment needs to be completed by 6:00 pm on Sunday 11/24/2019 all screen shots are attached to this question

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Linear Model-TechnologyTips
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To complete the Linear Model portion of the project, you will need to use technology (or hand-drawing) to create a scatterplot, find the regression line, plot the regression line, and
find r and r2.
Below are some options, together with some videos. Each video is limited to 5 minutes or less. It takes a bit of time for the video to initially download. When playing the
video, if you want to slow it down to read the text, hit the pause icon. (If you run the mouse over the bottom of the video screen, the video controls will appear.) You may
need to adjust the volume.
The basic options are to:
(1) Generate by hand and scan.
(2) Use Microsoft Excel.
Visit Scatterplot – Start (VIDEO) to see how to create a scatter plot using Microsoft Excel and format the axes.
Visit Scatterplot – Regression Line (VIDEO) to see how to add labels and title to the scatterplot, how to generate and graph the line of best fit (regression) and obtain the
value of r2 in Microsoft Excel.
Using Excel to obtain precise values of slope m and y-intercept b of the regression line: Video, Spreadsheet
(3) Use Open Office.
(4) Use a hand-held graphing calculator (See section 2.5 in your textbook for help with Texas Instruments hand-held calculators.)
(5) Use a free online tool
Use the free Desmos calculator: See DesmosLinearRegressionGuide.pdf to view how to generate a scatterplot and carry out linear regression.
The result of the free tool might not be as nice looking as the Microsoft Excel version, but it is free.
The Linear Project Example uses Microsoft Excel.
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Summer Olympics: Men’s 400 Meter Dash Winning Times
44.80
44.60
y = -0.025x + 93.834
R2 = 0.5351
44.40
44.20
Time (seconds)
44.00
43.80
43.60
43.40
1968
1976
1984
1992
2000
2008
Year
Using the most recent ten winning times, our regression line is y = -0.025x + 93.834.
When x = 2012, the prediction is y=-0.025(2012) + 93.834 –43.5 seconds. This line predicts a winning time of 43.5 seconds for 2012 and
that would indicate an excellent time close to the existing record of 43.49 seconds, but not dramatically below it.
Linear ModelExample
Note too that for p = 0.5351 and for the negatively sloping line, the correlation coefficient is r = -✓
-V0.5351 = -0.73, not as strong as when
we considered the time period going back to 1948. The most recent set of 10 winning times do not visually exhibit as strong a linear trend as the
set of 16 winning times dating back to 1948.
1
CONCLUSION:
I have examined two linear models, using different subsets of the Olympic winning times for the men’s 400 meter dash and both have
moderately strong negative correlation coefficients. One model uses data extending back to 1948 and predicts a winning time of 43.1 seconds
for the 2012 Olympics, and the other model uses data from the most recent 10 Olympic games and predicts 43.5 seconds. My guess is that 43.5
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(LR-5) Values of r2 and r:
= 0.6991
We know that the slope of the regression line is negative so the correlation coefficient r must be negative.
r = -V
-10.6991
= -0.84
Recall that r=-1 corresponds to perfect negative correlation, and so r=-0.84 indicates moderately strong negative correlation
(relatively close to -1 but not very strong).
(LR-6) Prediction: For the 2012 Summer Olympics, substitute x = 2012 to get y=-0.0431(2012) + 129.84
43.1 seconds.
The regression line predicts a winning time of 43.1 seconds for the Men’s 400 Meter Dash in the 2012 Summer Olympics in London.
(LR-7) Narrative:
The data consisted of the winning times for the men’s 400m event in the Summer Olympics, for 1948 through 2008. The data exhibit
a moderately strong downward linear trend, looking overall at the 60 year period.
The regression line predicts a winning time of 43.1 seconds for the 2012 Summer Olympics, which would be nearly 0.4 second less
than the existing Olympic record of 43.49 seconds, quite a feat!
Linear ModelExample
Will the regression line’s prediction be accurate? In the last two decades, there appears to be more of a cyclical (up and down)
trend. Could winning times continue to drop at the same average rate? Extensive searches for talented potential athletes and
improved full-time training methods can lead to decreased winning times, but ultimately, there will be a physical limit for humans.
Note that there were some unusual data points of 46.7 seconds in 1956 and 43.80 in 1968, which are far above and far below the
regression line.
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(LR-3)
Summer Olympics: Men’s 400 Meter Dash Winning Times
47.00
46.50
y = -0.0431x + 129.84
R2 = 0.6991
46.00
45.50
Time (seconds)
45.00
44.50
44.00
43.50
43.00
1944
1952
1960
1968
1976
1984
1992
2000
2008
Year
Line of Best Fit (Regression Line)
y=-0.0431x + 129.84 where x = Year and y = Winning Time (in seconds)
(LR-4) The slope is -0.0431 and is negative since the winning times are generally decreasing.
The slope indicates that in general, the winning time decreases by 0.0431 second a year, and so the winning time decreases at an
average rate of 4(0.0431) = 0.1724 second each 4-year Olympic interval.
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(Sample) Curve-Fitting Project – Linear Model: Men’s 400 Meter Dash
Submitted by Suzanne Sands
(LR-1) Purpose: To analyze the winning times for the Olympic Men’s 400 Meter Dash using a linear model
Data: The winning times were retrieved from http://www.databaseolympics.com/sport/sportevent.htm?sp=ATH&enum=130
The winning times were gathered for the most recent 16 Summer Olympics, post-WWII. (More data was available, back to 1896.)
DATA:
(LR-2) SCATTERPLOT:
Summer Olympics: Men’s 400 Meter Dash Winning Times
47.00
Summer Olympics:
Men’s 400 Meter Dash
Winning Times
Time
Year (seconds)
1948
46.20
46.50
46.00
1952
45.90
46.70
1956
45.50
1960
44.90
Time (seconds)
45.00
1964
45.10
1968
43.80
44.50
1972
44.66
1976
44.26
44.00
1980
44.60
43.50
1984
44.27
1988
43.87
43.00
1944
1992
43.50
1952
1960
1968
1976
1984
1992
2000
2008
1996
43.49
Year
43.84
2000
2004
44.00
As one would evnect the winning times generally showa downward trend as stronger comnetition and training