Statistical Analysis of Body Temperature and Heart Rate Data Report

This report presents a statistical inquiry and analysis of body temperature and heart rate data, including 130 samples with gender as a categorical variable. The analysis begins with a research proposal exploring potential differences in body temperature and heart rate between males and females, and the relationship between these variables. Descriptive statistics, including summary statistics, box plots, and histograms, are provided to visualize the data's distribution. Inferential statistics, such as t-tests and Pearson correlation tests, are employed to investigate significant correlations between body temperature and heart rate, and to determine if there are significant differences in mean body temperatures and heart rates between genders. The analysis also includes regression analysis to model the relationship between heart rate and body temperature, assessing the significance of the model parameters and checking model assumptions. The report concludes with references to relevant statistical resources.

Statistics Inquiry and Analysis

Data Description:
The Data on body temperature by stat crunch is selected for statistical
inquiry and analysis. The data has 3 variable among which one is categorical. The Gender
variable has two categories of male and female. The other variables are body temperature
and heart rate. The body temperature measured in Fahrenheit and the heart rate in bpm.
The size of the data is 130.
Research Proposal:
One might be interested in the question whether the body temperate in
the given sample differs from male to female. We might be interested in similar question
regarding heart rate. Further we can check if there is any relation between temperature and
heart rate.
Descriptive Statistics:
The following is the summary statistics for body temperature and heart rate. It seems there
is no much difference between the body temp of male and female, which needs to be
confirmed by the test. In heart rate, even though the means look to be near for male and
female, there is huge difference in variances.
Summary statistics for Body Temp:
Group by: Gender
Gender n Mean Variance Std. dev. Std. err. Median Range Min Max Q1 Q3
Female 65 98.393846 0.55277404 0.74348775 0.092218306 98.4 4.4 96.4 100.8 98 98.8
Male 65 98.104615 0.48825962 0.69875576 0.086669986 98.1 3.2 96.3 99.5 97.6 98.6
Summary statistics for Heart Rate:
Group by: Gender

Gender n Mean Variance Std. dev. Std. err. Median Range Min Max Q1 Q3
Female 65 74.153846 65.694712 8.1052274 1.0053297 76 32 57 89 68 80
Male 65 73.369231 34.517788 5.8751841 0.7287269 73 28 58 86 70 78
The best graphical view of summary statistics, box plots are shown below.
The following is the histogram drawn for Body Temperature. The given skewness and
kurtosis gives the extent the data deviates from standard normal.
Column Skewness Kurtosis
Body Temp-0.00441913120.7804574

The following diagram is the histogram of the heart rate and its deviation from the standard
normal is measured by skewness and kurtosis values mentioned below.
Summary statistics:
Column Skewness Kurtosis
Heart Rate-0.17835296-0.46302097

An easy way to see the normality of the data is to see the Q-Q plot. The Q-Q plot shows that
the data are normally distributed which can be seen by the data points lying near to the line
A scatter plot is displayed below for Body Temperature Vs Heart Rate. It shows a positive
correlation between the variables.

Correlation between Body Temp and Heart Rate is: 0.2536564(p-value=0.0036)
The pair plot above shows how Body Temp and Heart Rate are related in each gender.
Correlation between Body Temp and Heart Rate for Female Gender = 0.28693115(p value=0.0205)
Correlation between Body Temp and Heart Rate for Male Gender = 0.19558938(p value=0.1184).
Inferential Statistics:
The first test is about checking whether there is significant correlation between the Body
Temp and Heart Rate.
Null Hypothesis: There is no correlation between the Body Temp and Heart Rate.
Alternative Hypothesis: There is correlation between the Body temp and Heart Rate.

We perform this test by using Pearson correlation test where we use t statistic. The p-value
of the test is 0.0036 < 0.05 and we can infer that there is significant correlation between the
variables ,Body Temp and Heart Rate variables.
When similar test is conducted with in each females and males, we got p-values as 0.021
and 0.118(>0.05) which means there is significant correlation among females and no
significant correlation among males.
In the second test, we are interested to know whether the mean values of Body Temp are
significantly different between males and females.
Null Hypothesis (Ho): There is no difference in the mean Body temperature of males and
females.
Alternative Hypothesis (H1): There is difference in the mean Body temperature of males and
females.
To test this hypothesis, we use t-test for 2 samples. The following is the output from the
software. Normality assumption is satisfied by both the samples (Shapiro-Wilk Normality
test).Equal variance assumption is also satisfied (Levene’s test).
The p-value of t test is 0.0239 < 0.05 which means we have enough evidence to reject Ho. or
otherwise we can conclude there is difference in the mean Body temperature of males and
females.
Two sample T hypothesis test:
μ1 : Mean of Body Temp(Male)
μ2 : Mean of Body Temp(Female)
μ1 - μ2 : Difference between two means
H0 : μ1 - μ2 = 0
HA : μ1 - μ2 ≠ 0
(without pooled variances)
Hypothesis test results:
Differ
ence
Sampl
e Diff.
Std.
Err.
DF T-
Stat
P-
val
ue
μ1 - μ2 0.28923
077
0.1265
5395
127.
5103
2.285
4345
0.02
39
Shapiro-Wilk normality test results:

Sampl
e
n Stat P-Value
var5 65 0.96797487 0.0902
var8 65 0.98940716 0.8545
Homogeneity of Variance results:
Data stored in separate columns.
Levene's Test for Homogeneity of Variance
Test
Statistic
DF
1
DF
2
P-value
0.061118127 1 128 0.8051
In the second test, we are interested to know whether the mean values of Heart Rate are
significantly different between males and females.
Null Hypothesis: There is no difference in the mean Heart Rate of males and females.
Alternative Hypothesis: There is difference in the mean Heart Rate of males and females
Again we use the t-test of samples and output from the software is given below. The
assumption of normality is satisfied (Shapiro-Wilk normality test). The p-value of the test is
0.587 which means we have to accept the null hypothesis. We can conclude that there is no
significant difference in the mean heart rates of males and females.
Two sample T hypothesis test:
μ1 : Mean of Heart Rate(Male)
μ2 : Mean of Heart Rate(Female)
μ1 - μ2 : Difference between two means
H0 : μ1 - μ2 = 0
HA : μ1 - μ2 ≠ 0

Hypothesis test results:
Differ
ence
Sampl
e Diff.
Std.
Err.
DF T-
Stat
P-
val
ue
μ1 - μ2 0.7846
1538
1.241
6645
116.7
0438
0.631
9061
0.5
287
Shapiro-Wilk normality test results:
Sampl
e
n Stat P-Value
var6 65 0.97206506 0.1483
var9 65 0.98813546 0.7912

Regression Analysis:
Consider the model y = a + b*x + error
Where y is heart rate, x is body temperature.
We want to significance of the model.
So we are interested in two hypothesis
i) Null: b =0
Alternative: b≠0
ii) Null: a = 0
Alternative: a ≠ 0
The following is the output from the software.
Simple linear regression results:
Dependent Variable: Body Temp
Independent Variable: Heart Rate
Body Temp = 96.306754 + 0.026334549 Heart Rate
Sample size: 130
R (correlation coefficient) = 0.2536564
R-sq = 0.064341571
Estimate of error standard deviation: 0.71196889
Parameter estimates:
Paramete
r
Estimate Std. Err. Alternativ
e
DF T-Stat P-value
Intercept 96.306754 0.65770318 ≠ 0128 146.4289 <0.0001
Slope 0.026334549 0.0088763359 ≠ 0128 2.9668265 0.0036

Analysis of variance table for regression model:
Sourc
e
DF SS MS F-stat P-value
Model 1 4.4617613 4.4617613 8.8020594 0.0036
Error 128 64.883162 0.5068997
Total 129 69.344923

From the output, we can see that the constant (a) and slope (b) are significantly different
from zero. The estimated line is given and residuals are normally distributed. We can see
from the graphs that all the assumptions of the simple linear regression are satisfied.

References
Anderson, T. W., & Finn, J. D. (1996). The new statistical analysis of data. New York:
Springer.
Mendenhall, W., & Sincich, T. (2003). A second course in statistics: Regression analysis.
Upper Saddle River, NJ: Pearson Education.
Pretorius, T. B. (1995). Inferential statistics: Hypothesis testing and decision-making. Cape
Town: Percept.
Rohatgi, V. K., & Saleh, A. K. (2015). An introduction to probability theory and statistics.
Hoboken, NJ: John Wiley & Sons.