SPSS STATISTICS: People's daily workout/exercises

STATISTICS WITH SPSS
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Introduction
A physical activity prescription is an under-utilized tool for improving community health. In the right dose, physical activity can help to prevent, treat, and manage a range of chronic health conditions that increasingly impact the quality of life and physical function of individuals on a global scale. Safe and effective exercise prescription requires careful consideration for the target individual's health status, baseline fitness, goals, and preferences. In the wake of the current global pandemic, the whole world at large has been confined to less movement and more indoor activities. In this sense, daily routines were disrupted overall. People that took advantage of walking to their workplaces have now been confined to sitting behind a computer as they work from home. Similarly, the few that spent their early morning hours outside jogging were also affected. Therefore, this study's main objective sought to evaluate current fitness practices. Moreover, the topic also aimed at answering the following questions;
* How long do they work out?
* What kind of exercises do they do?
* Where do they do?
* How often do they do the workout?
Data
An online survey was designed to obtain data on the physical fitness routines of a random population. The questions in the survey entailed;
* Age
* Gender
* Daily length of time spent working out (time_mins)
* Types of exercises (type)
* Weekly frequency of exercising (frequency)
* Location of the physical activities (location)
The answers from the participants were collected as data and entered according to each participant’s answers. A total of 100 observations and 6 variables formed the final dataset. From the dataset, various tests were conducted to meet the topic's interest and answer the above questions as detailed in the next section.
Methodology
First and foremost, the data was uploaded into SPSS from which it would be analyzed. Notably, the variables were classified differently where;
* Age, time_mins, and frequency were ratio
* Gender and type were nominal
* Location was ordinal.
Also, gender and age were included in the online survey questionnaire as two forms of disaggregation (demographic details of the participants). Discriminately, participants aged below 18 and above 60 were dropped from the survey because there would be huge variations and outliers. Also, the fact that age 18 to 60 are ideal for fitness activities and would provide valuable data for the study. Nevertheless, there was the presence of outliers in the time_mins variable.
For descriptive statistics, the mean, median, variance, and standard deviation were obtained for age, time_mins, and frequency as they were numerical. For the rest of the variables, their count, frequency, and percentages were obtained as they were categorical. To visualize the variables' distributions, a boxplot, histogram, and pie chart were plotted for each class of variable. Two-way tables were formulated for categorical variables. Normal and Student t distribution were tested in numerical variables. Application of confidence intervals on numerical variables was also conduc...