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Scatter Plot:LE
The relationship is negative, as life expectancy rate increases the immortality rate decreases.  smayweather074  May 20, 2019  174B  16 
Angela Whitiker Lab 6 (Scatter Plot)
A pediatrician wants to determine the relation that exists between a child's height, x, and head circumference, y. She randomly selects 11 children from her practice, measures their heights and head circumferences, and obtains the accompanying data.
Plot Interpretation
A close glimpse of the scatter plot data and the regression line reveals a positive upward trend of the Height measurement as a function of the Head circumference relationship.
Analysis of the correlation coefficient ( r ) value derived from the Height vs. Head Circumference data validates the above observation. With a value ( r = 0.8881 ), a compelling case can be made that for the data under consideration a positive linear relationship exists between the two variables (Height vs. Head Circumference). Although the relationship is not altogether perfect ( for r = 1.000 ), nevertheless there exits a strong relationship.
Regression Line Model
Y = 0.184 X + 12.494
For the linear equation above representing the regression line where X (the independent variable) represents Height and Y (the dependent variable) represents Head Circumference. The slope value (m = 0.184) represents a positive upward relationship between the two variables where for each increase of one inch in Height, correspondingly there is going to be an increase of 0.184 inch in Head circumference. The value of 12.494 represents the value that would have existed given a height of 0 inch, but in this case or situation this value has no practical significance. In other words no normal child would exist with a body height of 0 inch.
As had been already discussed previously for the Height vs. Head Circumference scenario, both the r value which indicate a positively correlation coefficient between the two variables and a visual inspection of the scatter plot validating that relationship, one could firmly conclude that the mean value of the Head Circumference could not be the same as the predicted value of the Head Circumference.
Coefficient of Determination
For the Height vs. Head Circumference scenario, the coefficient of determination was calculated and evaluated to be about 0.7887 ( or 78.87% ) suggesting the percentage of the total variation in the Head Circumference that is explained by the variation in the Height variable in the regression model. Alternatively, a large Coefficient of Determination value implies that the explained variation is a large portion of the total variation.
Proportion of Variability
R2 = r2 = (0.8881) 2 = 0.7887 ( or 78.87% )
Again for the present scenario, the Proportion of Variability value in the Head Circumference explained by the relation between Height and Head Circumference is 78.87%.
 awhitiker  May 10, 2019  174B  36 
Dependent Variable: Head Circumference (inches), y Independent Variable: Height (inches), x Head Circumference (inches), y = 12.815228 + 0.17800338 Height (inches), x Sample size: 11 R (correlation coefficient) = 0.86755459 Rsq = 0.75265096 Estimate of error standard deviation: 0.1246616
Parameter  Estimate  Std. Err.  Alternative  DF  TStat  Pvalue 

Intercept  12.815228  0.90294248  ≠ 0  9  14.192741  <0.0001  Slope  0.17800338  0.034014595  ≠ 0  9  5.2331473  0.0005 
Source  DF  SS  MS  Fstat  Pvalue 

Model  1  0.42558991  0.42558991  27.38583  0.0005  Error  9  0.13986464  0.015540515    Total  10  0.56545455    
95% lower limit for mean response stored in new column: 95% L. Limit Mean 95% upper limit for mean response stored in new column: 95% U. Limit Mean
Simple Linear Regression
According to data I am sure that there is a positive relationship between Height and Head Circumference from the equation y= 12.815 0.178x. Coefficent of Determation better know as the r value is 0.868 (rounded to three places). The Proportion of Variabilty known as r^2 = 0.753.  mwilson147  Apr 4, 2019  3KB  40 
BMI and Heart Rate Relationship Scatter Plot  malonso11  Feb 10, 2019  174B  16 
Rows: Confidence Columns: Tatoos
Cell format 

Count (Column percent) 
 No  Yes  Total 

Neither confident nor not confident  93 (9.41%)  43 (10.91%)  136 (9.84%)  Somewhat confident  543 (54.96%)  223 (56.6%)  766 (55.43%)  Somewhat not confident  32 (3.24%)  10 (2.54%)  42 (3.04%)  Very confident  311 (31.48%)  113 (28.68%)  424 (30.68%)  Very not confident  9 (0.91%)  5 (1.27%)  14 (1.01%)  Total  988 (100%)  394 (100%)  1382 (100%) 
Statistic  DF  Value  Pvalue 

Chisquare  4  2.3115238  0.6787 
Relationship with confidence and tattoos
Yasmin N  ynaylor  Jan 8, 2019  3KB  17 
Count = 1124
marstat3r  Frequency  Relative Frequency 

Married/Cohabiting (incl. same sex couples)/Civil Partner  609  0.54181495  Single  243  0.21619217  Widowed/ Divorced/ Separated (incl. same sex couples)  272  0.24199288 
Count = 1124
agex  Frequency  Relative Frequency 

16 to 24  89  0.079181495  25 to 44  388  0.34519573  45 to 54  169  0.15035587  55 to 64  184  0.16370107  65 to 74  151  0.13434164  75 and over  143  0.1272242 
Count = 1124
Count = 808
Frequency Table (relationships, age, work, work hours)  5933509df9cef2de3768e03e67800d3442e155b5_canvas_indmlp  Dec 2, 2018  4KB  14 
Lab 6 Jessica Hall Simple Linear Regression
Our E of 0.862 shows that there may be a strong linear relationship between the head size of a child and height . However, the chart shows that as the height of the child increases so does the head size. In addition, the slope is 0.137 , so for every 1 inch increase in height there is a .137 increase in head circumference . So based on the data I can predict that if a baby height is 32 inches , that their head circumference would be about 18.11 inches .  jessicasymone26  Nov 10, 2018  174B  63 
Antoinette Riley Lab 6 Page 2 Simple Linear Regression Numerical Analysis
Our R of 0.831 shows that there may be a strong linear relationship between the head size of a child and height . However, the chart shows that as the height of the child increases so does the head size. In addition, the slope is 0.147 , so for every 1 inch increase in height there is a .147 increase in head circumference . So based on the data I can predict that if a baby height is 32 inches , that their head circumference would be about 18.06inches .  antoinettenriley  Nov 10, 2018  174B  41  Simple linear regression results:
Dependent Variable: MMNT Independent Variable: MMXT MMNT = 96.505588 + 0.94176507 MMXT Sample size: 12 R (correlation coefficient) = 0.98866784 Rsq = 0.9774641 Estimate of error standard deviation: 15.404855 Parameter estimates:
Parameter  Estimate  Std. Err.  Alternative  DF  TStat  Pvalue 

Intercept  96.505588  7.5313761  ≠ 0  10  12.813805  <0.0001  Slope  0.94176507  0.045219899  ≠ 0  10  20.826342  <0.0001  Analysis of variance table for regression model:
Source  DF  SS  MS  Fstat  Pvalue 

Model  1  102929.82  102929.82  433.73651  <0.0001  Error  10  2373.0956  237.30956    Total  11  105302.92    
Simple Linear Regression  886c67dbc8a44949b3c689c7c51c558d88051_d2l_snhumlp  Oct 14, 2018  3KB  23 
Bar Plot With Summary relationship between deaths and community size  45249611_ecollege_baker  Jun 6, 2018  174B  55 
Bar Plot With Summary relationship between # of deaths and tornado intensity  45249611_ecollege_baker  Jun 6, 2018  174B  79 
Simple Linear Regression
For every 1 unit increase of x, there is a 25 increase in y. R=3000(approximately) is very strong which means that y=2.0x  2.3 is a good predictor for the data set. For example, if x=10, we can predict that y=2.6(10)2.325=24. rounded . Alternatively, if r was weak we could use ybar=1000 as the better predictor for x. For instance, if you input any value of x, y would equal 1500. 90% of the proportion of variability in the yvariable is explained by the relationship between (x,y) which is the regression line. The higher the number the more confident we are in  clinitka12  May 9, 2018  174B  124 
EDGAR VACA  Simple Linear Regression LAB 6 ANALYSIS
Based on the data provided on the scatter diagram, the correlation coefficient is close to zero it can be deduced that little or no evidence exists of a linear relationship between age and [HDL] cholesterol level. If age increases by one year, the [HDL] cholesterol decreases about 0.189. This data is very useful to predict the future numbers. For example, we roughly predict the age of 70, the [HDL] cholesterol can decrease to 40.3. R is 0.155, therefore, 0% of the variation in age and [HDL] the regression line can explain cholesterol. This data sample is not a reliable sample because it is not an accurate measure for the correlation variables between age and [HDL] cholesterol levels. If R were closer to 1 we could make future predicts of HDL cholesterol levels with a higher certainty  evaca6  Dec 17, 2017  174B  118  Simple linear regression results:
Dependent Variable: HDL Cholesterol Independent Variable: Age HDL Cholesterol = 52.875  0.125 Age Sample size: 5 R (correlation coefficient) = 0.047746895 Rsq = 0.002279766 Estimate of error standard deviation: 13.902937 Parameter estimates:
Parameter  Estimate  Std. Err.  Alternative  DF  TStat  Pvalue 

Intercept  52.875  64.014643  ≠ 0  3  0.82598289  0.4694  Slope  0.125  1.5097625  ≠ 0  3  0.082794478  0.9392  Analysis of variance table for regression model:
Source  DF  SS  MS  Fstat  Pvalue 

Model  1  1.325  1.325  0.0068549256  0.9392  Error  3  579.875  193.29167    Total  4  581.2    
Predicted values stored in new column: Predicted Values
Kayode Balogun's Simple Linear Regression
This graph represents the relationship between age and HDL Cholesterol. The plot shows that no linear relationship exists because the correlation coefficient is approximately 0.0477. The correlation of determination is 52.875% making the results unreliable in measuring these variables. For every increase in years, the HD cholesterol will decrease by 0.125, which extremely close to 0 making the relationship obsolete. A 49yearold will decrease by approximately 53.  kay_bal10  Nov 11, 2017  3KB  164 
Prob. 4.30 Simple Linear Regression
a) scatter plot
b) linear correlation coefficient = 0.958 (rounded to three decimals)
c) Does a linear relationship exist between atmospheric pressure and wind speed? Yes.  stephore  Nov 6, 2017  174B  78 

