StatCrunch logo (home)

Results shared by StatCrunch members
Showing 1 to 15 of 170 results matching relationship
Name/Notes Owner Created Size Views
Scatter Plot:LE
The relationship is negative, as life expectancy rate increases the immortality rate decreases.
smayweather074May 20, 2019174B16
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%.
awhitikerMay 10, 2019174B36

Simple linear regression results:


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
R-sq = 0.75265096
Estimate of error standard deviation: 0.1246616

Parameter estimates:


ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept12.8152280.90294248 ≠ 0914.192741<0.0001
Slope0.178003380.034014595 ≠ 095.23314730.0005

Analysis of variance table for regression model:


SourceDFSSMSF-statP-value
Model10.425589910.4255899127.385830.0005
Error90.139864640.015540515
Total100.56545455

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.
mwilson147Apr 4, 20193KB40
BMI and Heart Rate Relationship Scatter Plotmalonso11Feb 10, 2019174B16

Contingency table results:


Rows: Confidence
Columns: Tatoos

Cell format
Count
(Column percent)

NoYesTotal
Neither confident nor not confident93
(9.41%)
43
(10.91%)
136
(9.84%)
Somewhat confident543
(54.96%)
223
(56.6%)
766
(55.43%)
Somewhat not confident32
(3.24%)
10
(2.54%)
42
(3.04%)
Very confident311
(31.48%)
113
(28.68%)
424
(30.68%)
Very not confident9
(0.91%)
5
(1.27%)
14
(1.01%)
Total988
(100%)
394
(100%)
1382
(100%)

Chi-Square test:


StatisticDFValueP-value
Chi-square42.31152380.6787
Relationship with confidence and tattoos
Yasmin N
ynaylorJan 8, 20193KB17

Frequency table results for marstat3r:


Count = 1124
marstat3rFrequencyRelative Frequency
Married/Cohabiting (incl. same sex couples)/Civil Partner6090.54181495
Single2430.21619217
Widowed/ Divorced/ Separated (incl. same sex couples)2720.24199288

Frequency table results for agex:


Count = 1124
agexFrequencyRelative Frequency
16 to 24890.079181495
25 to 443880.34519573
45 to 541690.15035587
55 to 641840.16370107
65 to 741510.13434164
75 and over1430.1272242

Frequency table results for DVILO3a:


Count = 1124
DVILO3aFrequencyRelative Frequency
Economically Inactive4420.39323843
ILO Unemployed550.048932384
In Employment6270.55782918

Frequency table results for FtPtWk:


Count = 808
FtPtWkFrequencyRelative Frequency
Full-time5760.71287129
Part-time2320.28712871

Frequency Table (relationships, age, work, work hours)
5933509df9cef2de3768e03e67800d3442e155b5_canvas_indmlpDec 2, 20184KB14
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 .
jessicasymone26Nov 10, 2018174B63
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 .
antoinettenrileyNov 10, 2018174B41
Simple linear regression results:
Dependent Variable: MMNT
Independent Variable: MMXT
MMNT = -96.505588 + 0.94176507 MMXT
Sample size: 12
R (correlation coefficient) = 0.98866784
R-sq = 0.9774641
Estimate of error standard deviation: 15.404855

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept-96.5055887.5313761 ≠ 010-12.813805<0.0001
Slope0.941765070.045219899 ≠ 01020.826342<0.0001

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model1102929.82102929.82433.73651<0.0001
Error102373.0956237.30956
Total11105302.92
Simple Linear Regression
886c67db-c8a4-4949-b3c6-89c7c51c558d-88051_d2l_snhumlpOct 14, 20183KB23
Bar Plot With Summary relationship between deaths and community size45249611_ecollege_bakerJun 6, 2018174B55
Bar Plot With Summary relationship between # of deaths and tornado intensity45249611_ecollege_bakerJun 6, 2018174B79
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 y-bar=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 y-variable is explained by the relationship between (x,y) which is the regression line. The higher the number the more confident we are in
clinitka12May 9, 2018174B124
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
evaca6Dec 17, 2017174B118
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
R-sq = 0.002279766
Estimate of error standard deviation: 13.902937

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept52.87564.014643 ≠ 030.825982890.4694
Slope-0.1251.5097625 ≠ 03-0.0827944780.9392

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model11.3251.3250.00685492560.9392
Error3579.875193.29167
Total4581.2

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 49-year-old will decrease by approximately 53.
kay_bal10Nov 11, 20173KB164
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.
stephoreNov 6, 2017174B78

1 2 3 4 5 6 7 8 9 10   >

Always Learning