1. introduction to statistics
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The variables and their corresponding measurement scales are: - Age: Ratio scale - Gender: Nominal scale - City: Nominal scale - Satisfaction level: Ordinal scale - Salary: Ratio scale - Marital status: Nominal scale - Number of children: Ratio scale
1. introduction to statistics
- 2. Statistics Statistics Descriptive statistics Inferential statistics Collection, Analysis, Interpretation & Presentation of data Collection, Organisation & Presentation of data to reach conclusions about the group that is choosen for study. Sample data is collected and statistics is used to reach conclusions about the population from which sample is taken
- 3. Population Sample In a movie theater there are 45 seats, you want to understand whether the movie was liked or disliked by the viewers. â You survey all the 45 members then you have have chosen the population. â You randomly pick 15 members then you have chosen a sample from the population. â Population is the complete collection of all members in a group. â Sample is a portion of the population. â Descriptive measure for the population is parameter. â Descriptive measure for the sample is statistic. Population versus sample
- 4. Data Data Recorded measurements about the group that is under study Numerical Categorical Ratio scale Ordinal scale Quantitative / Continous data Qualitative / Discrete data Interval scale Nominal scale
- 5. Data Numerical Ratio scale Quantitative / Continous data Interval scale â Numerical data ranging from (-â to +â) â There exists absolute zero (0) â Ratio of two numbers is meaningful â Distances between number has meaning â Operations allowed (+, -, *, /) â Highest level of data measurement â Example: Age, Weight, Number of customers visiting store, Revenue generated â Numerical data ranging from (-â to +â) â There doesnât exist absolute zero ( 0 point is only represented as convention and not a natural or fixed zero) â Ratio of two numbers is not meaningful â Distances between number has meaning â Operations allowed (+, -) â Next to highest level of data measurement â Example: Calendar days, Centigrade scale
- 6. Data Categorical Ordinal scale Qualitative / Discrete data Nominal scale â Categorical or discrete (Non numeric / Nonmetric data) â Distances between categories has no meaning â Order of categories has meaning â Operations allowed (<, >) â Level of data measurement is higher than nominal â Example: Very unhappy < Unhappy < Ok < Happy < Very happy â Categorical or discrete (Non numeric / Nonmetric data) â Distances between categories has no meaning â Order of categories has no meaning â Operations allowed (=, â ) â Lowest level of data measurement â Example: Sex, Student ID, State, Country
- 7. Data Question: Identify data measurement scale for each variable in the given table below
- 8. Data Question: Identify data measurement scale for each variable in the given table below Interval Nominal Interval Nominal Nominal Ratio Ratio OrdinalNominal