This document provides information about statistical analysis tools in Microsoft Excel and statistical concepts. It lists several common statistical software packages used in research and their purposes. It also defines measures of central tendency, variability, and position. The document discusses using Excel's Data Analysis tools to analyze data and lists some common tools. It provides an example of using a t-test for correlated groups to compare results before and after an induced treatment. Overall, the document discusses statistical concepts and analysis using Excel.

1. Gerald D. Soriano
Research Teacher

3. 1. Candy was sweet

4. 2. Bug was 5cm long

5. 4. Mass of the beaker was 122g

6. 5. My fingernail is 2cm long

7. 6. The slug was slimy

8. 7. Laptop is white

9. 8. He ran 50 miles

10. 9. The plant is green

11. 10. Kalamansi tastes sour

12. 11.One leaf is 9cm long

13. 12. The rainbow is colorful

14. 13. Table sugar is too sweet

15. 14. Flower clusters in 3 blooms

16. 15.Leaves are stiff and dry

17. 16. Plant is short

18. 17.Its 27 degrees Celsius

19. 18. My cat is 5 years old

20. 19. The plant has pink flowers

21. 20.I caught 15 foot shark

22. Task: Compute and Compare
Directions: Given the hypothetical data below, compute for the
average of first quarter grades using manual calculation and for
second quarter grades, using a calculator. Note the time started and
time ended for each calculation.
First Quarter Grades Second Quarter
Grades
90 94
95 98
98 98
96 97
95 97
94 95
93 93
97 98

23. Time Started Time Ended
Average of
first quarter
grades using
manual
calculation.
Average of
second
quarter
grades using
calculator.
Which method is faster?
Which do you think will give you more accurate results?
What do you think are some ways to make our calculation easier?

24. Manual calculation has always been very
difficult for most if not all. For obvious reasons, it
has triggered some software developers to come
up with some statistical packages or software.
Our demand to raise our standards in
education and facilitate learning, makes us more
agitated to integrate technology in the teaching-
learning experiences of teachers and students.

25. Teaching with technology typically
involves utilizing a variety of IT and
multimedia resources for online learning,
course management, electronic course
materials, and novel tools of
communication, engagement,
experimental, critical thinking, and
assessment (Dinov, 2006).

26. According to Fansworth (2019), the following are
the most common statistical software used in
various field of research:
1. SPSS (IBM) - (Statistical Package for the Social
Sciences) the most widely used statistics
software package. SPSS offers the ability to
easily compile descriptive statistics, parametric
and non-parametric analyses, as well as
graphical depictions of results through the
graphical user interface (GUI).

27. 2. R (R Foundation for Statistical
Computing) - widely used across both human
behavior research and in other fields.
Toolboxes are available for a great range of
applications, which can simplify various
aspects of data processing. It also has a steep
learning curve, requiring a certain degree of
coding.

28. 3. MATLAB (The Mathworks) - an
analytical platform and programming
language that is widely used by
engineers and scientists. A plentiful
number of toolboxes are also
available to help answer your
research questions.

29. 4.SAS (Statistical Analysis Software) -
offers options to use either the GUI, or to
create scripts for more advanced analyses.
It is a premium solution that is widely
used in business, healthcare, and human
behavior research alike. It’s possible to
carry out advanced analyses and produce
publication-worthy graphs and charts.

30. 5. GraphPad Prism - used within statistics
related to biology, but offers a range of
capabilities that can be used across various
fields.
6. Minitab - offers a range of both basic and
fairly advanced statistical tools for data
analysis. Similar to GraphPad Prism,
commands can be executed through both the
GUI and scripted commands.

31. 7. MS Excel does offer a wide variety of tools for
data visualization and simple statistics. It’s
simple to generate summary metrics and
customizable graphics and figures, making it a
usable tool for many who want to see the basics
of their data. As many individuals and
companies both own and know how to use
Excel, it also makes it an accessible option for
those looking to get started with statistics.

32. Data analysis is a process that involves
examining and investigating collected data
for interpretation to discover relevant
information, drawing or proposing
conclusions, and supporting decision-
making to solve a research problem
(Green, et al., 2007).

33. Measures of Central Tendency,
Position and Variability

34. Measures of Central
Tendency
Mean
Median
Mode

35. Measures of
Variability
Range
Variance
Standard
Deviation

36. Measures of Position
Quartile
Decile
 Percentile

37. Go to Microsoft excel and click Data Analysis. List down the
data analysis tools that you can find.
If the Data Analysis could not be found or not activated, you may proceed with
the following steps.
1. Click file 4. Choose analysis tool pack
2. Click options 5. Click OK
3. Select Add ins 6. You may now go back with data tab and check if
Data Analysis has been successfully activated.
Guide Questions:
•What are the different analysis tools you are familiar with from the list you
have created? Encircle them.
•What is your prior knowledge about the use of MS Excel?
•What is the advantage of using MS Excel result in the interpretation of data?

38. t-Test for Two Correlated Groups
This test is used when only two correlated
groups are being compared and the
measurements are either interval or ratio. Same
samples are observed before and after the
introduction of the independent variable
whereas same samples are used in both
experimental and control group.

39. Before
(no induced
extract)
After
(induced with
extract)
Sample A 34 25
Sample B 25 24
Sample C 45 44
Sample D 56 53
Sample E 70 65

40. Recall on your Research I regarding how the
null hypothesis should be rejected or accepted.
The decision rule is: If the tstat or tcalc is greater
than the tcrit or ttab, the null hypothesis is
rejected and the alternative hypothesis is
accepted. Therefore, there is a significant
difference between the two treatments. On the
other hand, if the tstat or tcalc is lesser than the
tcrit or ttab, the null hypothesis is accepted.
Therefore, there is no significant difference
between two treatments.

41. Accomplish written
work 2 and
performance task
2.Please check our
google classroom for
some updates and
tasks to be
accomplished.