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Mathematics & Economics
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Statistics Project
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English (U.S.)
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Topic:
SPSS STATISTICS: People's daily workout/exercises
Statistics Project Instructions:
How long do they work out? What kind of exercises do they do? Where do they do? How often do they do workout?
**** Update from the C: Just do some research on the Internet. I don't have any other information. I'll send you the data tomorrow.
Statistics Project Sample Content Preview:
STATISTICS WITH SPSS
Student’s Name
Course
Professor’s Name
Date
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 conducted. Lastly, a correlation on numerical variables and linear regression was conducted where the frequency was the explanatory variable.
Analysis and Results
Age
time_mins
frequency
N Valid
100
100
100
Missing
0
0
0
Mean
34.15
51.80
3.86
Median
32.50
30.00
3.00
Std. Deviation
11.398
38.779
2.113
Variance
129.907
1503.798
4.465
From the above table, 100 observations were used to obtain the above measures of central tendencies and dispersion. There were no missing values in all variables. Time_mins had the highest mean (x̅2 =51.8) while age had the highest median (x̃1 =32.5). Frequency had the lowest featured descriptive statistics while time_mins had the highest standard deviation (s2=38.8) and variance (s22 =1503.8).
As mentioned earlier, a boxplot, histogram, and pie chart were plotted for location, frequency, and type. The plots below were extracted from SPSS.
Recall that a boxplot shows a five-number summary of data. In this case, frequency's mean, median, quartiles, and ranges were plotted in a box and whisker. There were no visible outliers and the median and mean were close to each other (see the lower width of the box). The maximum and minimum (range) were largely distant (see the whiskers) as the 1st and 3rd quartiles (box's widths). Based on the histogram, the frequencies of location differed significantly. Of the 100 participants, those who exercised indoors were more than those who did so outdoors. And on the pie chart, aerobics was most preferred to flexibility while balance and strength ranked 2nd and 3rd.
For the categorical variables, two two-way frequency and relative tables were obtained. Gender*location and location* type tables were formulated as shown in the SPSS results.
From the gender*location table, 14 females and 37 males preferred indoors exercise to outdoors. So, 13 females and 36 males went outdoors to exercise. In total, 27 females and 73 males participated in the survey. For aerobics (39), 18 opted for indoors while 21, outdoors. From the group of 26 participants who chose balance, 10 exercised indoors as 16 exercised outdoors. 3 flexibility fanatics chose indoors while 12 of them opted for outdoors. No participant who chose strength as a type of exercise went outdoors. In total, 51 participants chose indoors while 49 opted for outdoors.
Skewness
Kurtosis
Statistic
Std. Error
Statistic
Std. Error
time_mins
.774
.241
-.675
.478
frequency
.109
.241
-1.185
.478
age
.450
.241
-.686
.478
Valid N (list-wise)
Using skewness and kurtosis, normal distribution was tested in time_mins, frequency, and age. See the table above. A normal distribution is illustrated by the skewness of 0 and kurtosis of 3. All three variables had a skewness and kurtosis far away from 0 but between 0.5 and 1. Their distributions were moderately skewed. Also, the kurtosis values were far away from 3, and hence, time_mins, frequency, and age were not normally distributed.
A Shapiro-Wilk test was added to affirm the conclusion of normality. The table below shows the results.
Kolmogorov-Smirnov
Shapiro-Wilk
Statistic
df
Sig.
Statistic
Df
Sig.
Frequency
.198
100
.000
.870
100
...
Student’s Name
Course
Professor’s Name
Date
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 conducted. Lastly, a correlation on numerical variables and linear regression was conducted where the frequency was the explanatory variable.
Analysis and Results
Age
time_mins
frequency
N Valid
100
100
100
Missing
0
0
0
Mean
34.15
51.80
3.86
Median
32.50
30.00
3.00
Std. Deviation
11.398
38.779
2.113
Variance
129.907
1503.798
4.465
From the above table, 100 observations were used to obtain the above measures of central tendencies and dispersion. There were no missing values in all variables. Time_mins had the highest mean (x̅2 =51.8) while age had the highest median (x̃1 =32.5). Frequency had the lowest featured descriptive statistics while time_mins had the highest standard deviation (s2=38.8) and variance (s22 =1503.8).
As mentioned earlier, a boxplot, histogram, and pie chart were plotted for location, frequency, and type. The plots below were extracted from SPSS.
Recall that a boxplot shows a five-number summary of data. In this case, frequency's mean, median, quartiles, and ranges were plotted in a box and whisker. There were no visible outliers and the median and mean were close to each other (see the lower width of the box). The maximum and minimum (range) were largely distant (see the whiskers) as the 1st and 3rd quartiles (box's widths). Based on the histogram, the frequencies of location differed significantly. Of the 100 participants, those who exercised indoors were more than those who did so outdoors. And on the pie chart, aerobics was most preferred to flexibility while balance and strength ranked 2nd and 3rd.
For the categorical variables, two two-way frequency and relative tables were obtained. Gender*location and location* type tables were formulated as shown in the SPSS results.
From the gender*location table, 14 females and 37 males preferred indoors exercise to outdoors. So, 13 females and 36 males went outdoors to exercise. In total, 27 females and 73 males participated in the survey. For aerobics (39), 18 opted for indoors while 21, outdoors. From the group of 26 participants who chose balance, 10 exercised indoors as 16 exercised outdoors. 3 flexibility fanatics chose indoors while 12 of them opted for outdoors. No participant who chose strength as a type of exercise went outdoors. In total, 51 participants chose indoors while 49 opted for outdoors.
Skewness
Kurtosis
Statistic
Std. Error
Statistic
Std. Error
time_mins
.774
.241
-.675
.478
frequency
.109
.241
-1.185
.478
age
.450
.241
-.686
.478
Valid N (list-wise)
Using skewness and kurtosis, normal distribution was tested in time_mins, frequency, and age. See the table above. A normal distribution is illustrated by the skewness of 0 and kurtosis of 3. All three variables had a skewness and kurtosis far away from 0 but between 0.5 and 1. Their distributions were moderately skewed. Also, the kurtosis values were far away from 3, and hence, time_mins, frequency, and age were not normally distributed.
A Shapiro-Wilk test was added to affirm the conclusion of normality. The table below shows the results.
Kolmogorov-Smirnov
Shapiro-Wilk
Statistic
df
Sig.
Statistic
Df
Sig.
Frequency
.198
100
.000
.870
100
...
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