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Transcript of Math~Graphs
The hypothesis statement is that we believe that as a person grows older, their height will increase.
Occurs when we want to determine relationship between two factors on this case.
Independent variable :Age.
It is affected by the data, and the value doesn't affect the variable.
Dependent variable :Height.
It is affected by the independent variable.
We used a sample of 154 S.T. Jean De Brebeuf students from different courses
The sampling technique that we used with two different variables ages and heights is a stratified sample where population was divided into shared common characteristic. We need to find a logic answer to the hypothesis statement.
Total population is 154 people with ages on 11,12,13,14,15,16,17,18,19,20,21,22,23,24,25 intervals
Total population is 154 people with heights on intervals
Standard deviation: 3.890793
age affects height
Video Human growth facts
1. What is your gender
2. What is your Age?
a)11-13 b)14-16 c)17-19 d)20-22 e)23-25
3.What is your height
a)4'6-4'10Feets b)4'10-5'2Feets c)5'2-5'6Feets d)5'6-5'10Feets e)5'10-5'12
Age Vs Height
Correlation of coefficient: 0.122309
Positive line correlation Weak
Therefore, the line of best fit for this data is
4. What is your Daily intake of food
a)1 meal b)2 meal c)3meal d)4meal e)5meal
5. Do you partake in physical activity
if yes, what are they?
6.How tall your parents?
7. Are you still growing?
a) Yes b)No
9.Do you do any heavy lifting
10. Have you been in accident or broken any bones
8.Who is the tallest person in you family, how tall are they:
Physical activity People
Still Growing People
any heavy lifting People
Some accident or broken any bones People
Standard deviation: 1.968138
Ages Vs Height
Ages Vs Heights
DEPENDENT : Heights
heights variable have frequency data points of all intervals.
Interval from 62-66 inches,they have a higher frequency.
The rest of the population has few data points.
we have a linear correlation of coefficient 0.122309
A direction positive and weak.
In this case the interpolation is a prediction
My present factors that goes with the hypothesis prove, if it is real a false
The results from the surveys meet the statistic theory.
The studied population of the independent variable ages had a greater frequency in the interval 14 to 16 years. The studied population of the dependent variable heights had a greater frequency in the interval 62 to 66 inches. The dependent and independent variable have a weak correlation, coefficient and as a result, the hypothesis is not possible.
Students in grade 10 to 12 with ages of 14-16 have a higher frequency.
No data points on ages intervals from 11-13. and 23-25
Between the ages 17-22 represent, the rest of the population with less frequency.
The hypothesis statement is not possible, when a person is growing older, and his height maybe or maybe not increase. the human body has a limited, where the person will stop growing.
Sample 154 students
Average ages: 16%
Average height: 65%
Positive Linear Correlation WEAK
In the moment when your mom is pregnant. You will grow until you reach the age of 21 approximately. This is time where you stop grow up