Statistics   lesson 1
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Statistics lesson 1

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Statistics   lesson 1 Statistics lesson 1 Presentation Transcript

  • Statistics
  • Statistics
    • Is a scientific body of knowledge that deals with:
      • collection of data
      • organization or presentation of data
      • analysis and interpretation of data
    • Is a statistical procedure concerned with describing the characteristics and properties of group of persons, places or things; it is based on easily verifiable facts.
    Descriptive Statistics
    • Is a statistical procedure used to draw inferences for the population on the basis of the information obtained from the sample.
    Inferential Statistics
    • Population. It is the total collection of all the elements (people, events, objects, measurements, and so on) one wishes to investigate.
    • Sample. Subgroup obtained from a population.
    • Parameter. A numerical value that describes a characteristic of a population.
    Definitions
    • Statistic. It is a numerical value that describes a particular sample.
    • Data. This are facts, or a set of information gathered or under study.
    • Quantitative Data are numerical in nature and therefore meaningful arithmetic can be done.
      • Ex: age
    Definitions
    • Qualitative Data are attributes which cannot be subjected to meaningful arithmetic.
      • Ex: gender
    • Discrete Data assume exact values only and can be obtained by counting
      • Ex: number of students
    Definitions
    • Continuous Data assume infinite values within a specified interval and can be obtained by measurement.
      • Ex: height
    • Constant is a characteristic or property of a population or sample which makes the member similar to each other.
    Definitions
    • Variable is a characteristic or property of a population or sample which makes the members different from each other.
    • Dependent. A variable which is affected by another variable.
      • Ex: test scores
    Definitions
    • Independent. A variable which affects the dependent variable.
      • Ex: number of hours spent in studying
    Definitions
  • Levels of Measurements
    • Nominal numbers do not mean anything; they just label.
      • Ex: SSS Number
    • Ordinal numbers are used to label + rank.
      • Ex: size of t-shirt
  • Levels of Measurements
    • Interval numbers are used to label + rank; do not have a true zero.
      • Ex: temperature
    • Ratio numbers are used to label + rank + equal unit of interval; have a true zero
      • Ex: number of votes
  • Target Practice
    • A. Determine whether the set of data is qualitative or quantitative.
    • Models of cell phones
    • Number of subscribers to Philippine Daily News
    • Weights of 1000 packs of a brand of noodles
    • Yes or No responses to survey question
    • Telephone number
  • Target Practice
    • B. Which of the following numbers is discrete or continuous?
    • Distance from town A to town B
    • Record of absent students in a class in Statistics
    • Number of customers in a restaurant
    • Number of cars parked in the basement of a building
    • Weights of all Grades 1 pupils in the Library School
  • Target Practice
    • C. Identify the level of measurement: nominal(N), ordinal(O), interval(I), or ratio(R) most appropriate for each of the following data.
    • Color of the eye
    • Number of votes
    • Rank of faculty
    • Exam score
    • Temperature in Baguio last summer
  • Determining the Sample Size Slovin’s Formula: n is the sample size N is the population size e is the margin of error The margin of error is a value which quantifies possible sampling errors.
  • Determining the Sample Size The margin of error can be interpreted by the use of ideas from the laws of probability. In reality, it is what statisticians call a confidence interval. Sampling error means that the results in the sample differ from those of the target population because of the “luck of the draw”.
  • Sampling Techniques Sampling is the process of selecting samples from a given population.
    • Probability Sampling
    • Non-probability Sampling
    Types:
  • Sampling Techniques
    • Probability Sampling: Samples are chosen in such a way that each member of the population has a known though not necessarily equal chance of being included in the samples.
      • Avoids biases
      • It provides the basis for calculating the margin of error.
  • Sampling Techniques
    • Simple Random Sampling: Samples are chosen at random with members of the population having a known or sometimes equal probability or chance of being included in the samples.
      • Lottery
      • Generation of random numbers
  • Sampling Techniques 2. Systematic Sampling: Samples are chosen following certain rules set by the researchers. This involves choosing the k th member of the population, with k=N/n, but there should be a random start.
  • Sampling Techniques 3. Cluster Sampling: is sometimes called area sampling because it is usually applied when the population is large. In this technique, groups or clusters instead of individuals are randomly chosen.
  • Sampling Techniques 4. Stratified Random Sampling: This method is used when the population is too big to handle, thus dividing N into subgroups, called strata , is necessary. A process that can be used is proportional allocation .
  • Sampling Techniques B. Non Probability Sampling: Each member of the population does not have a known chance of being included in the sample. Instead, personal judgment plays a very important role in the selection. Non-probability sampling is one of the sources of errors in research.
  • Sampling Techniques Types:
    • Convenience Sampling: This type is used because of the convenience it offers to the researcher.
    • Quota Sampling: This is very similar to the stratified random sampling. The only difference is that the selection of the members of the samples in stratified sampling is done randomly.
  • Sampling Techniques 3. Purposive Sampling: Choosing the respondents on the basis of pre-determined criteria set by the researcher.
  • Data Gathering Techniques
    • The Direct or the Interview Method: In this method, the researcher has direct contact with the researcher.
    • A: Clarification can be done easily.
    • D: Costly and time-consuming.
  • Data Gathering Techniques
    • The Indirect or Questionnaire Method: The researcher gives or distributes the questionnaire to the respondents either by personal delivery or by mail.
    • A: Saves time and money; large number of samples can be reached.
    • D: Problem of retrieval
  • Data Gathering Techniques
    • The Questionnaire (characteristics)
    • It should contain a short letter to the respondents which includes:
      • a. The purpose of the survey
      • b. An assurance of confidentiality
      • c. The name of the researcher or writer of the questionnaire
  • Data Gathering Techniques The Questionnaire (characteristics) 2. There is a descriptive title/name for the questionnaire. 3. It is designed to achieve objectives. 4. The directions are clear 5. It is designed for easy tabulation.
  • Data Gathering Techniques The Questionnaire (characteristics) 6. It avoids the use of double negatives. 7. It also avoids double barreled questions. 8. It phrases questions well for all respondents.
  • Data Gathering Techniques
    • Types of Questionnaire
    • Open – this type has an unlimited responses
    • Closed – this type limits the scope of responses
    • Combination – this type is a combination of open and closed types of questionnaire
  • Data Gathering Techniques
    • Types of Questions
    • Multiple choice – allows respondent to select answer/s from the list
    • Ranking – asks respondents ton rank the given items
    • Scales – asks respondents to give his/her degree of agreement to a statement (Likert-scale)
  • Data Gathering Techniques 3.The Registration Method: This method of gathering data is governed by laws. A: Most reliable source of data D: Data are limited to what are listed in the documents
  • Data Gathering Techniques 4. The Experimental Method: This method of gathering data is used to find out cause and effect relationships. A: Can go beyond plain description D: Lots of threats to internal and external validity
  • Presentation of Data Textual Form: Data are presented in paragraph or in sentences. This includes enumeration of important characteristics, emphasizing the most significant features and highlighting the most striking attributes of the set of data.
  • Presentation of Data Tabular Form: A more effective device of presenting data. 1. stem and leaf plots 2. frequency distribution table 3. contingency table
  • Presentation of Data Graphical/Pictorial Form: A most effective device of presenting data. 1. line graph (freq. polygon, ogive) 2. bar graph (histogram) 3. pie chart 4. pictograph 5. statistical maps