introduction to probability and statistics consultation time: ms. chong
TRANSCRIPT
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Introduction to Probability and Statistics
Consultation time:
Ms. Chong
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OUTLINEOUTLINE
1-1 Introduction1-2 Descriptive and Inferential
Statistics1-3 Variables and Types of Data1-4 Data Collection and Sampling
Techniques1-5 Computers and Calculators
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OBJECTIVESOBJECTIVESDemonstrate knowledge of all statistical
terms.
Differentiate between the two branches of statistics.
Identify types of data.
Identify the measurement level for each variable.
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Identify the four basic sampling techniques.
Explain the importance of computers and calculators in statistics.
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1-1 Introduction1-1 Introduction
What is statistics?
Number ?
Value ?
Data ?
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Statistics consists of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data.
Data are the values (measurements or observations) that the variables can assume.
Variables whose values are determined by chance are called random variables.
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1-2 Descriptive and Inferential 1-2 Descriptive and Inferential StatisticsStatistics
Descriptive statistics consists of the collection, organization, summarization, and the presentation of data.
Inferential statistics consists of performing estimations and making decision about the whole population based on the information gained from limited samples.
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Involve the use of
“statistical terms” for the
data descriptions.
“Describe” the data that
have been collected.
Descriptive Descriptive StatisticsStatistics
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Examples of Descriptive Statistics
Six out of ten students in UCSI are local students.
Average score for Biology test is 75.8.
The average annual salary for staffs in Company A is RM 2500.
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Draw conclusions and make decisions
Make predictions
Inferential Inferential StatisticsStatistics
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Examples of Inferential Statistics
The population in Malaysia will increase by 5% in the coming five years.
It was estimated that the number of cars sold in next month is 50.
Making predictions and estimations
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Relief constipati
on problem.
Patients with
constipation
problem.
Eat diet high in fiber.
Examples of Inferential Statistics
Diet high in fiber can help to reduce constipation problem.
Making Conclusion
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A population consists of all subjects (human or otherwise) that are being studied.
A sample is a group of subjects selected from a population.
Population
Sample
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• Generally, researchers generalize their findings on population based on the sample selected.
• The sample must be representative enough to reflect the whole population.
• The sample is considered representative if:
The sample size was large enough. The sample was selected randomly (no bias).
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1-3 Variables and Types of Data1-3 Variables and Types of Data
Data
Qualitative Quantitative
Discrete Continuous
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Qualitative VariablesQualitative Variables
Qualitative variables are variables that can be placed into distinct categories, according to the attribute or characteristic.
It also can be known as non-numerical variables.
Example: Gender
Nationality
Marital status
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Quantitative VariablesQuantitative Variables
Quantitative variables are numerical and can be ordered or ranked.
It also can be known as numerical variables.
Example: Height
Age
Blood Pressure
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Discrete VariablesDiscrete Variables
Discrete variables assume values that can be counted.
Example: Number of student, pairs of shoes, price, etc.
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Continuous VariablesContinuous Variables
Continuous variables can assume all values between any two specific values.
They are obtained by measuring instead of counting.
Example: Weight, height, volume, pressure, etc.
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Level of MeasurementLevel of Measurement
Classify data according to how they are categorized, counted, or measured.
There are four level of measurement.
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Nominal
Interval
Ratio
Ordinal
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Norminal level of measurementNorminal level of measurement
Classify data into mutually exclusive categories in
which no order or ranking can be imposed on the data.
Example: Course studied (Medicine/Food Science, etc.)
Eye colour (brown/blue, etc.)
Gender (Male/Female)
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Ordinal level of measurementOrdinal level of measurement
Classify data into categories that can be ranked;
however, no precise differences exist between
the ranks.
Example: Grade (A, B, C, D)
Performance (poor, fair, good, excellent)
Judging (first place, second place, etc.)
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PerformancePerformance
Poor Fair Good Excellent
Is the difference between poor and fair same as the difference between good excellent?
Are the differences measurable?
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Interval level of measurement
Ranks data, and precise differences between the
units of measure do exist.
There is no true / meaningful zero.
Example: IQ (109, 110, etc.)
Temperature (- 5oC, 0oC, 8oC, 10oC, etc.)
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Ratio level of measurement
Possesses all characteristics of interval measurement.
A true zero do exists.
Additionally, true ratios exist for the same variable.
Example: Salary (RM0, RM1000, RM3000, etc.)
Weight (0kg, 35kg, 70kg, etc.)
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1-4 Data collection and 1-4 Data collection and Sampling Techniques Sampling Techniques
Data can be collected via different methods.
Survey is one of the most commonly used methods.
Surveys can be done through telephone, mail questionnaire, personal interviews, etc.
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Sampling MethodsSampling Methods
There are four basic sampling methods are used by statisticians to obtain samples that are unbiased.
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Random SamplingRandom Sampling
The samples are selected by chance methods or random numbers.
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Systematic SamplingSystematic Sampling
1 2 3 4…10…15…20…25.…30…35
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Cluster SamplingCluster Sampling
The population is divided into many clusters.
Samples are selected by using some intact groups that are representative of the population.
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POPULATION
A C
D
B
E
A C
E
Samples
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Stratified SamplingStratified Sampling
Population is divided into many groups (strata) according to some characteristics.
The subjects within groups are randomly selected as samples for study.
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POPULATION
A
C
B
D
A
B
C
D
50 samples
50 samples
50 samples
50 samples
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1-5 Computers and Calculators1-5 Computers and Calculators
Computers and calculators make numerical computation easier.
There are many statistical packages available such as Microsoft Excel, MINITAB, SPSS, Unscrambler, etc.
Data must still be understood and interpreted.