Total fat has the strongest correlation to calories in candy at 0.8. High cholesterol in candy is not strongly linked to saturated fat levels, with a correlation of only 0.47. Calories are more impacted by total fat than sugar, with correlations of 0.81 for total fat and 0.42 for sugar.

1. WHICH NUTRITIONAL VALUE AFFECTS THE AMOUNT OF
CALORIES IN CANDY THE MOST?
OR WHY YOU GET SO MANY CALORIES FROM A CANDY WITH
HIGH CONTENTS OF TOTAL FAT
Total Fat
Prepared by:
Alexander
Voronin
Bonnie Pang
Kayla Mina
Stephanie
HeintzmanImage source: Google Images

2. EXECUTIVE SUMMARY
Calories in candy products are highly impacted by
Total Fat (0,8 correlation)
High levels of cholesterol in candies aren’t connected
to the contents of saturated fat (0,47 correlation)
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3. TABLE OF CONTENTS
1. Our Data….……………………………………………………………4-
5
2. Data
Assumptions……………………………………………………….6
 Correlation coefficient as method of
research…………………………..7-8
 Using SAS to get insights about the
data………………………………..9-12
3. Conclusions about the
data.………………………………………..........13
4. How you can reach
3

4. BACKGROUND
Our data set consists of 75 candies.
For each candy, the following information is available:
Servings, Weight, Calories, Total Fat, Saturated Fat,
Cholesterol, Sodium, Carbohydrates, Fiber, Sugars,
Protein, Vitamin A, Vitamin C, Calcium, and Iron.
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5. HERE’S HOW OUR DATA
LOOKS IN SAS
5

6. WE HYPOTHESIZE THAT…
1. The more Saturated Fat there is,
the more Cholesterol there is.
2. Calories are impacted more by
Total Fat than by Sugar.
6

7. METHOD
We will use the correlation coefficient (r) to
indicate the strength of the relationships
This will be done using the analytical software,
SAS Enterprise Guide
7

8. HOW WE USE THE
CORRELATION COEFFICIENT?
It measures the strength and the direction of a linear
relationship between two variables
When the correlation is positive (r > 0), it means that
as the value of one variable increases, so does the
other.
If a correlation is negative (r < 0), it indicates that
when one variable increases, the other variable
decreases. This means there is an inverse relationship
between the two variables.
[Shen, David. "Computation of Correlation Coefficient and It's Confidence Interval in SAS." Sas.com. Web. ]
8

9. STEP 1
Open data set
Analyze > Multivariate > Correlations…
9

10. STEP 2
Window opens
Drag the indicated
variables under “Variables
to assign” to the Analysis
variable <variable
required> under “Task
roles”
10

11. STEP 3
Under the Results tab to
the left, check “Create a
scatter plot for each
correlation pair” and
uncheck “Show significance
probabilities associated
with correlations”.
Finally, Run the correlation
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12. RESULTSEach cell in the following data output shows the strength of the relationship
between the variables listed in the corresponding rows and columns.
The higher the number the stronger the relationship is
Numbers in the orange box link to
hypothesis 1Numbers in the red box link to hypothesis
2
12
Highest number in the dataset
– strongest relationship

13. CONCLUSIONS
Hypothesis 1- The more Saturated
Fat there is, the more Cholesterol
there is.
0.47270 – SatFat & Cholesterol
The relationship between
Saturated Fat and Cholesterol is
47%. This is a weak correlation.
This means that having more
Cholesterol does not indicate
higher Saturated Fat levels, and
vice versa.
Therefore we reject this
hypothesis.
Hypothesis 2 – When a candy is
high in calories, there is more
likely to be higher levels of Total
Fat than Sugar.
0.80707 – Calories & Total Fat
0.41692 – Calories & Sugar
The relationship is about 2X
stronger between calories and Total
Fat (80%), than Calories and Sugar
(41.6%).
This means that the higher levels of
Calories can be more likely
determined by the levels of Total Fat
than levels of Sugar.
Therefore we accept this hypothesis. 13

14. HOW YOU CAN REACH US
Our email address: team3@dataresearch.com
Feel free to contact us for data research using other
analytical tools and approaches
Using data analysis we’re able to find other
correlations in your dataset
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