Data Analysis

The following tables and graphs are created via SPSS.
(click to enlarge)


Data entry:

 DATA VIEW

 VARIABLE VIEW

TESTING NULL HYPOTHESIS

Step 1: State the null hypothesis

There is NO significant relationship between oral and axillary temperature.

a) If p value ≤ alpha (α), null hypothesis is rejected.

b) If p value > alpha (α), null hypothesis should be accepted

We have set alpha (α) = 0.05

CRITERIA IN REJECTING OR ACCEPTING NULL HYPOTHESIS

Step 2: Identify the variables level of measurement

Variable:
Oral temperature: Scale
Axillary temperature: Scale


Step 3: Select the measure of association

DECISION PATH

We chose Pearson's R as both our variables are scale variables.

Pearson's R is a symmetrical measure of association for interval level variables.
Pearson's correlation coefficient range from -1.0 (perfect negative relationship) to +1.0 (perfect positive relationship).

TABLE TO MEASURE RELATIONSHIP STRENGTH

Step 4: Compute the test values

SCATTER PLOT OF ORAL AND AXILLARY TEMPERATURE

SCATTER PLOT WITH FIT LINE AT TOTAL

SCATTER PLOT ANALYSIS

P < α, hence null hypothesis is rejected.

Step 5: Make decision and restate the statement

 

The table shows Pearson's coefficient of .828

The association is r =.828, p=0.000, N=30.

Therefore, there IS a positive, very strong relationship between a person's oral and axillary temperature.

COMPUTE REGRESSION EQUATION

y = mx + c

oral temperature = 0.930* (axillary temperature) + 2.726

Therefore, we conclude that there IS a positive, very strong relationship between a person's oral and axillary temperature.