Statistics deals with the collection, organization, presentation, analysis, and interpretation of numerical data. Descriptive statistics summarize and describe data without generalizing, while inferential statistics makes generalizations using data. There are various methods for collecting data, including direct interviews, indirect questionnaires, observation, experimentation, and registration. Data can be presented textually, in tables, or graphically. Population is the total set of data, while a sample is a subset. A census collects all data while a survey uses a sample. Variables can be quantitative or qualitative, discrete or continuous. Measurement scales include nominal, ordinal, interval, and ratio levels. Sources of error include sampling errors from the sampling process and non-sampling errors from other factors.

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2. STATISTICS – deals with the collection, organization, presentation, analysis
and interpretation of numerical data used as information for
decision making.
Descriptive Statistics – is a field of statistics that does not involve any
generalization. Branch of science that deals with
the methods concerned with the collection and description of data .This
includes any thing done to the data that is designed to summarize or
describe it without attempting to infer anything that goes beyond it.

3. Inferential Statistics- makes use of generalizations, predictions,
estimations or approximations in the face of
uncertainty .

4. Methods of Collecting Data
1. Direct method- data is collected through the use of interviews. The
enumerator talks to the subject personally. He gets the data through
a series of questions asked from the subject of the interview.
2. Indirect method- data is collected through the use of questionnaires.
3. Observation- Information is gathered by recording the behavior ,
attitude , or attribute of items, persons or group of items or persons
at the time of occurrence.

5. Methods of Collecting Data
4.) Experimentation-data is usually gathered through experiments in
laboratories and classrooms.
5.) Registration-data are acquired from private and government
agencies such as from the National Statistics Office,
the Bangko Sentral ng Pilipinas, Department of
Finance, etc.

6. Ways in Presenting Data
1. Textual form- data and information are presented in paragraph
and narrative form.
2. Tabular form- Quantitative data are summarized in rows and
columns.
3. Graphical form- data are presented in charts, graphs or pictures.

7. Year Level Number of Students
First Year 35
Second Year 50
Third Year 48
Fourth Year 24

8. Population and Sample
Population- is a set of all data that characterizes some phenomenon
of interest. That is, the totality or collection of all
elements to be studied. The population is also the
universe set..
Sample – is a representative portion of the population

9. Census – is the process of gathering information from every unit in the
population.
Survey-is the process of obtaining a representative portion of the
population.

10. Variable –is a characteristics that changes or varies over time and
for different individuals or objects under consideration.
Quantitative and Qualitative Variable
Qualitative variable- measures a characteristics on each individual or object
Quantitative variable-measure a numerical amount on each individual or
objects

11. Discrete and Continuous Variable
Discrete variable- can assume only a finite or countable number of
values.
Continuous variable- can assume an infinite number of values
corresponding to the point on a line interval.

12. Measurement Scales
1.) nominal level -the first level of measurements that consists of names, labels
or categories only in which no order or ranking can be imposed.
Example: Gender (male and female ), Marital Status (single, married, separated)
,employment (business, construction, engineering, education and etc. )
2.) ordinal level- data measured can be ordered or rank but precise differences do not
exist.
Example : Income Distribution (low income, middle income and upper income),
Body build (small, medium ,large)
.

13. Measurement Scales
3.) interval level –consist of data that may be arranged and meaningful amount of
differences between data values can be determined, however,
there is no meaningful zero.
Example: Temperature, score in a particular examination
4.) ratio level- consist of data that may be arranged and meaningful amounts of
differences between data values can be determined and ratios
between data values are meaningful.
Example :Weight, Height, Age
.

14. Determine which level is most appropriate in measuring each of the following
data.
1. SSS number
2. Weight of a package
3. Size of a family
4. t-shirt size (small, medium, large, extra large)
5. religion
6. Speed of a car in km/hr
7. SASE rating.

15. Two sources of errors
1. Sampling Errors
2. Non-sampling errors

16. Sampling errors- result from the actual sampling process such as
sampling techniques, small sample size and the fact that no sample
can be expected to be perfect represntation of the entire population.
Non-sampling errors- arise from other external factors not related to
sampling, such as a defective measuring instrument, missing values,
error in coding or recording data, or a discovered bias in the sample.

17. Sample design- is a definite plan, determined before any data are
actually collected for obtaining a sample from a given population.

18. Methods of Sampling
1. Non-probability Sampling-is a procedure of sampling wherein some elements of
the population have no possibility of being drawn into
the sample.
1. Probability Sampling-Is a process of sampling wherein each element in the
population and each possible sample has a nonzero
probability of selection.

19. Non-Probability Sampling
1.) Purposive
2.) Judgemental
3.) Quota
4.) Convenience
5.) Snowball or Chain

20. Purposive-select sample that agrees with the profile of the population based on some
pre-selected characteristics.
Judgemental-select a sample on the basis an “ expert’s” opinion, or on the judgement
of the person or people talking to the sample.
Quota- select a specified number of units possessing certain characteristics with the
actual selection being left to the researcher’s discretion.

21. Convenience- sometimes called accidental, grab or opportunity sampling.Use results
that are readily available.
Snowball or Chain- select a sample where existing study subjects are used to recruit
more subjects into the sample.

22. Probability Sampling
random sampling- the process of selecting random sample.

23. Simple random sampling- most frequently used and simplest probability sampling
procedure. In simple random sampling , all the 𝑁
𝑛
possible
samples are equally likely.
Steps in Generating a Simple Random Sample:
1. Number the elements of the population from 1 to N
2. Select n numbers from 1 to N using a random process recording each number to
identify the corresponding population element to be included in the sample.

24. Systematic random sampling-every kth element of the population is selected with the first unit being
selected at random.
Steps in Generating a System Random Sample
1. Number the elements of the population from 1 to N
2.Determine the sampling interval k, by 𝑘 =
𝑁
𝑛
3. Select at random the first element (random start) r of the sample from the first k elements of
the population. That is 1≤ 𝑟 ≤ 𝑘.
4. take every kth element from the random start as part of the sample.
5. Continue the process until the required number of samples is acquired.

25. Stratified random sampling
Steps in generating a stratified random sample of size n from a population of N elements
(Proportional and Optimum Allocation)
1. Classify the population into homogeneous strata
2. Draw a sample from each homogeneous stratum
3. The sample size 𝑛𝑖 of the 𝑖 𝑡ℎ stratum of size N, from a population of size N is :
𝑛𝑖 =
𝑁 𝑖
𝑁
× 𝑛

26. Cluster Random Sampling-one or more partitions are selected at random and random samples of elements from
each of the selected partitions are drawn.
Steps in generating a cluster random sampling
1. Partition the population into cluster.
2. Select at random one or several clusters.

27. CHAPTER II

28. DEFINITION
Raw Data- data collected in original form
Frequency Distribution Table-is a summary of the distribution of observations in a systematically
organized rows and columns.
1. One-way Frequency Distribution-tabular presentation where data are grouped or categorized into different
classes and then the number of observations that fall in each of the classes is recorded.

29. 2. Two-way Frequency Distribution- the data are grouped according to two variables. It is also called a cross-
tabulation or a contingency table.
Distribution of respondents according to Year Level and Gender

30. Weights (in kg) of Statistics and Probability Students
63 59 43 60 41 53 56 81
50 66 62 52 49 48 52 40
64 64 47 53 47 54 62 56
58 53 50 47 79 70 45 47
46 58 56 55 56 45 73 49

31. Construction of Frequency Distribution
1. Find the range (R) of the raw data: the range is the difference between the largest value and the smallest
value. That is,
R=(highest value) - (lowest value)
2.Determine the class interval, k
𝒌 = 𝑵 or 𝒌 = 𝟏 + 𝟑. 𝟑𝟐𝟐 𝒍𝒐𝒈 𝟏𝟎 𝐍 where 𝑁=number of observations
Note :( round off 𝒌 to the nearest whole number)
3. Determine the class size or class width, c: This is obtained by dividing the range of the raw data by the number
of classes. But the result is rounded up to the nearest higher value whose precision is the same as those of the
raw data.
𝑐 =
𝑅
𝑘

32. Construction of Frequency Distribution
4. Construct the lower class limit and the upper class limits.
List the lower class limit (LL) of the first class. The starting lower limit could be the lowest value or
any smaller number close to it.
List the lower limits of the succeeding classes by simply adding c (the class width) to the lower limit
of the preceding class.
The upper limit (UL) of the first class can then be obtained by subtracting one unit of measure from
the lower limit of the next class. The upper limits of the rest of the classes can be then obtained in a
similar fashion or by adding c to the upper limit of the preceding class.

33. Construction of Frequency Distribution
5. Tally the frequencies (𝑓𝑖) for each class constructed
a. Class Boundaries (CB) – If the data are continuous, the CB’s reflect the continuous proerty of the
data. The CB’s are obtained by taking the midpoints of the gaps between classes.
LCB= LL- 1
2 × 𝑜𝑛𝑒 𝑢𝑛𝑖𝑡 𝑜𝑓 𝑚𝑒𝑎𝑠𝑢𝑟𝑒
UCM= UL + 1
2 × 𝑜𝑛𝑒 𝑢𝑛𝑖𝑡 𝑜𝑓 𝑚𝑒𝑎𝑠𝑢𝑟𝑒
b. Class Mark ( 𝑥𝑖) – is the midpoint of a class or interval
𝑥𝑖 = 1
2 𝐿𝐿 + 𝑈𝐿 or 𝑥𝑖 = 1
2 𝐿𝐶𝐵 + 𝑈𝐶𝐵
c Relative frequency- is the frequency of a class expressed in proportion to the total number of
observation
𝑅𝐹 = 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 ÷ 𝑁

34. d. Cumulative Frequency (𝐹𝑖)- is a accumulated frequency of a class. It is the total number of
observations whose values do not exceed the upper limit or boundary
of the class.