1. IB Math Studies – Topic 6
Statistics

2. IB Course Guide Description

3. IB Course Guide Description

4. IB Course Guide Description

5. Describing Data Types of Data
Categorical Data – Describes a particular quality or characteristic. It can be divided into categories.
i.e. color of eyes or types of ice cream
Quantitative Data – Contains a numerical value. The information collected is termed numerical data.
Discrete – Takes exact number values and is often the result of counting.
i.e. number of TVs or number of houses on a street
Continuous – Takes numerical values within a certain range and is often a result of measuring.
i.e. the height of seniors or the weight of freshman

6. Types of Distribution Symmetric Distribution
Positively Skewed Distribution
Negatively Skewed Distribution

7. Example 1: Describing Data
24 families were surveyed to find the number of people in the family. The results are: 5, 9, 4, 4, 4, 5, 3, 4, 6, 8, 8, 5, 7, 6, 6, 8, 6, 9, 10, 7, 3, 5, 6, 6
Is this data discrete or continuous?
Construct a frequency table for the data.
Display the data using a column graph.
Describe the shape of the distribution. Are there any outliers?
What percentage of families have 5 or fewer people in them?

8. Standard Deviation Formula
x is any score
is the mean
n is the number of scores

9. Calculate the standard deviation
Values 2 4 5 6 7 35
Calculate the mean
Subtract the mean from each value
Square these
Add them
Divide by n
Take the square root

10. Standard Deviation on the GDC
Type data in List 1
1-Var Stats L1
On paper you’ll see ‘s’ being used to standard for standard deviation.
But you should use the σ measurement from the calculator.

11. Measuring the Spread of Dara
The median is the second quartile, Q2
or 50th percentile
The lower quartile, Q1, is the median of the lower half of the data
or 25th percentile
The upper quartile, Q3, is the median of the upper half of the data
or 75th percentile
The inter-quartile range is the difference in the upper quartile and the lower quartile.
IQR = Q3 – Q1

12. Box Plots The inter-quartile range is the width of the box.
The maximum length of each whisker is 1.5 times the inter-quartile range.
Any data value that is larger than (or smaller than) 1.5 × IQR is marked as an outlier.

13. To Create a Box-and-Whisker Plot:
Make a number line.
Create the box between Q1 and Q3.
Draw in Q2.
Determine any outliers:
Upper boundary = Q (IQR)
Lower boundary = Q1 – 1.5(IQR)
Plot any outliers.
Extend the whiskers to the maximum & minimum (provided they’re not outliers).

14. Example :Box and Whisker Plots
A hospital is trialing a new anesthetic drug and has collected data on how long the new and old drugs take before the patient becomes unconscious. They wish to know which drug acts faster and which is more reliable.
Old drug times:
8, 12, 9, 8, 16, 10, 14, 7, 5, 21, 13, 10, 8, 10 11, 8, 11, 9, 11, 14
New drug times:
8, 12, 7, 8, 12, 11, 9, 8, 10, 8, 10, 9, 12, 8, 8, 7, 10, 7, 9, 9
Prepare a parallel box plot for the data sets and use it to compare the two drugs for speed and reliability.

15. FORMULA Pearson’s Correlation Coefficient: r

16. Correlation Coefficient on the GDC
Turn on your Diagnostics
Enter the data in L1 and L2
LinReg L1, L2

17. Example 1: Correlation Coefficient
In an experiment a vertical spring was fixed at its upper end. It was stretched by hanging different weights on its lower end. The length of the spring was then measured. The following readings were obtained.
Load (kg) x 1 2 3 4 5 6 7 8
Length (cm) y
23.5 25
26.5 27
28.5
31.5
34.5 36
37.5
(i) Write down the mean value of the load,
(ii) Write down the standard deviation of the load.
(iii) Write down the mean value of the length,
(iv) Write down the standard deviation of the length.
Nov 2006, paper 2, question 3
It is given that the covariance Sxy is
(d) (i) Write down the correlation coefficient, r, for these readings.
(ii) Comment on this result.

18. Average speed in the metropolitan area and age of drivers
Example 2: Correlation Coefficient
Average speed in the metropolitan area and age of drivers
The r-value for this association is Describe the association.

19. Drawing the Line of Best Fit
Calculate mean of x values , and mean of y values
Mark the mean point on the scatter plot
Draw a line through the mean point that is through the middle of the data
equal number of points above and below line

20. Least Squares Regression Line
Consider the set of points below.
Square the distances and find their sum.
we want that sum to be small.
The regression line is used for prediction purposes.
The regression line is less reliable when extended far beyond the region of the data.

21. Line of Regression using GDC
LinReg(ax +b) Test, L1, L2
where L1 contains your independent data.
and L2 contains your dependent data

22. Example 3: Line of Regression
The table shows the annual income and average weekly grocery bill for a selection of families
Construct a scatter plot to illustrate the data.
Use technology to find the line of best fit.
Estimate the weekly grocery bill for a family
with an annual income of £95000.
Comment on whether this estimate is likely to be reliable.
Example 7 on page 610 of second edition

23. X2 Test of Independence The variables may be dependent:
Females may be more likely to exercise regularly than males.
The variables may be independent:
Gender has no effect on whether they exercise regularly.
A chi-squared test is used to determine whether two variables from the same sample are independent.

24. How to do it:
Write the null hypothesis (H0) and the alternate hypothesis (H1).
Create contingency tables for observed and expected values.
Calculate the chi-square statistic and degrees of freedom.
Find the chi-squared critical value (booklet).
Depends on the level of significance (p) and the degrees of freedom (v).
Determine whether or not to accept the null hypothesis.

25. Contingency Tables Observed Frequencies Expected Frequencies Column1
Totals
Row1 a b
sum row1
Row2 c d
sum row2
Sum column1
Sum column2
total
Column1
Column2
Totals
Row1
sum row1
Row2
sum row2
sum column 1
sum column 2
total

26. Χ2 Statistic on the GDC On the calculator:
Put your contingency table in matrix A
STAT
TESTS
C: χ2 Test
Observed: [A]
Expected: [B] (this is where you want to go)
Calculate
Output:
Χ2  Χ2 calculated value
df  degrees of freedom
in Matrix B  expected values

27. Find the Critical Value
Get this from the formula booklet.
Significance level (p) is always given in the problem.
A 5% significance level = 95% confidence level
Degrees of freedom: v = (c - 1)(r – 1)
where c = number of columns in table
and r = number of rows in table

28. Accepting the Null Hypothesis
If X2calc < Critical Value
ACCEPT the null hypothesis
If X2calc > Critical Value
REJECT the null hypothesis
Hoffman’s train of thought:
Rejection is bad
Want calc value to be smaller than booklet value
(the calculator is physically smaller than the booklet)

29. Important IB Notes:
In examinations: the value of sxy will be given if required.
sx represents the standard deviation of the variable X;
sxy represents the covariance of the variables X and Y.
A GDC can be used to calculate r when raw data is given.
For the EXAM students do NOT need to know how to find the covariance.
But, for their project if they’re doing regression, then they DO need to do covariance by hand so they can do the r by hand so they can get points for using a sophisticated math process.