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Basic Statistics in 1 hour.pptx
- 1. BASIC STATISTICS IN ONE HOUR
- 2. SESSION FLOW What is Statistics? Population & Sample What is Data? Types of Data Level of Measurements Summary Statistics Types of Charts Presentation of data Univariate Analysis Bivariate Analysis
- 3. Statistics Statistics is the science concerned with developing and studying methods for collecting, analysing, interpreting and presenting data.
- 4. Population is the entire group that you want to draw conclusions about. Sample is a subset of a population that contains characteristics of that population.
- 5. Method of selecting sample from the population is called Sampling method.
- 6. What is Data ? Data is a collection of facts or information from which conclusions may be drawn.
- 7. Data Laal Singh Chaddha (Aamir Khan) is that passenger on your train who has a lot of stories to tell, even if you don’t want to be part of it. That’s how the story starts by Laal making the viewers the co-passengers on a train to Chandigarh and starting to narrate his journey from a dim-witted guy wearing leg-braces to the front-page celebrity of a famous magazine. Laal grows up with just one person Rupa (Kareena Kapoor Khan) who actually gets him after his mother (Mona Singh). Cust ID Gender Age Region Source Payment Product Amount Time Of Day 10001 Male 38 East TV advt Credit Card Books 617 22:19 10002 Female 25 West Email Paypal Clothing 3083 13:27 10003 Male 24 North Email Net Banking Grocery 1762 14:27 10004 Male 33 West Email Paypal Home Kitchen 2248 15:38 10005 Male 21 South TV advt Cash On Delivery Grocery 1299 15:21 10006 Male 28 West Web Paypal Mobile 13041 13:11 10007 Male 20 East Email Paypal Mobile 14455 21:59 10008 Female 20 West TV advt Credit Card Home Kitchen 13090 04:04 10009 Female 38 West TV advt Cash On Delivery Grocery 16322 19:35 10010 Male 26 South Newspaper Credit Card Grocery 11716 13:26 10011 Female 27 South Newspaper Paypal Home Kitchen 18176 14:17 10012 Male 45 East Newspaper Credit Card Books 15505 01:01 10013 Male 58 North Email Cash On Delivery Books 21649 10:04 10014 Male 49 East Email Debit Card Home Kitchen 18227 09:09 10015 Female 29 West Email Net Banking Clothing 10971 05:05 10016 Male 19 West TV advt Credit Card Clothing 12956 20:29
- 8. Types of Data Qualitative or Attribute data - the characteristic being studied is nonnumeric. E.g.: Gender, religious affiliation, state of birth, condition of patient, words, images, videos. Quantitative data - the characteristic being studied is numeric. E.g.: time (in seconds) for 400 mts race, age of corona patient, no. of WBC in blood sample.
- 9. Quantitative Data Discrete variables: can only assume certain values. E.g.: no. of pregnancies, no. of missing teeth in children of a school, no. of visits made by doctor ,the number of goals in a football match, the number of wickets by a bowler in a cricket match. Continuous variable can assume any value within a specified range. E.g.: the height of an athlete or the weight of a boxer, skull circumference, diastolic blood pressure, serum- cholesterol.
- 11. Levels of Measurements • Nominal • Ordinal Categorical • Interval • Ratio Scale / Numeric
- 12. Nominal-Level Data Properties: • Observations of a qualitative variable can only be classified and counted. • There is no particular order to the labels. E.g. Blood group, Marital status, Eye colour, Gender, Religion Favorite beverage Group Membership
- 13. Ordinal-Level Data Properties: • Data classifications are represented by sets of labels or names (high, medium, low) that have relative values. • Because of the relative values, the data classified can be ranked or ordered. E.g. Stage of disease, Severity of pain, level of satisfaction, Likert scale
- 14. Interval-Level Data Properties: • Data classifications are ordered according to the amount of the characteristic they possess. • Equal differences in the characteristic are represented by equal differences in the measurements. E.g. Temperature , SAT score, Shoe size, Dress Size, distance from landmark, geographical coordinates ( longitudes, latitudes) Dress Size
- 15. Ratio-Level Data Properties: • Data classifications are ordered according to the amount of the characteristics they possess. • Equal differences in the characteristic are represented by equal differences in the numbers assigned to the classifications. • The zero point is the absence of the characteristic and the ratio between two numbers is meaningful. E.g. Head circumference, Time until death, weight, Kelvin temperature Height Weight
- 18. Decide Level of Measurement
- 19. • Sex: nominal • Blood group: nominal • BMI: numerical • BMI group: ordinal • Number of courses: numerical • Body temperature: numerical
- 22. Types of Charts
- 23. Pie Chart The pie (circle) represents 100% of the variable and is divided into sectors. The area of each sector represents the frequency of each category in the variable it represents.
- 24. Bar Chart Bar graphs are more commonly used to represent categorical variables. It can be vertical or horizontal graphs and can show the frequency or the percentage of each category.
- 25. Histogram It is similar to the bar chart, but there are no gaps between the bars as the variable is continuous. The width of each bar of the histogram relates to a range of values for the variable, but in most cases, the width is kept the same.
- 26. Scatter Diagram If we have two variables that are numerical, the relationship between them can be illustrated using a scatter diagram. It plots one variable against the other in a two-way diagram. One variable is represented on the horizontal axis and the other is plotted on the vertical axis with each dot representing one case.
- 27. Box-Whisker Plot The boxplot (also called Box and Whisker plot) is used to summarize numerical variables based on the five-number summary. Those five numbers are minimum, maximum, median, upper quartile, and lower quartile.
- 28. Which Chart ? ONLY ONE VARIABLE SCALE CATEGORICAL SCALE HISTOGRAM SCATTER PLOT BOX-PLOT CATEGORICAL PIE / BAR BOX-PLOT MULTIPLE / STACKED
- 31. Univariate Analysis Univariate analysis is a basic kind of analysis technique for statistical data. Here the data contains just one variable. The main objective of the univariate analysis is to describe the data in order to find out the patterns in the data. Some of the measures in Univariate Analysis: • Central Tendency • Dispersion • Skewness • Kurtosis
- 32. Central Tendency The Mean of a variable can be computed as the sum of the observed values divided by the number of observations. The Median is the point at the centre of the data, where half of the values are above, and half are below it. The Mode is the most frequently occurring value in the dataset Measures that indicate the approximate centre of the data are called Measures of Central Tendency.
- 33. Dispersion The Range is simply the difference between the largest and smallest values. The Inter-Quartile Range is simply the difference between the upper quartile and the lower quartile The Variance is an average of squared deviations from mean. Standard deviation is calculated as the square root of the variance Measures that describe the spread of the data from central tendency are Measures of Dispersion.
- 34. Skewness Normal distribution Positively Skewed Negatively Skewed Skewness is a measure of symmetry, or more precisely, the lack of symmetry.
- 35. Kurtosis Kurtosis is a statistical measure used to describe the degree to which observations cluster in the tails or the peak of a frequency distribution.
- 36. Choosing Summary Statistics Type of Variable Scale Normally distributed Mean (Standard deviation) Skewed data Median (Interquartile range) Categorical Ordinal: Median (Interquartile range) Nominal: Mode (None)
- 37. Bivariate Analysis Bivariate analysis is stated to be an analysis of any concurrent relation between two variables or attributes. This study explores the relationship of two variables as well as the depth of this relationship to figure out if there are any discrepancies between two variables and any causes of this difference. Some of the measures in Bivariate Analysis: • Correlation • Regression • Time Series
- 38. Correlation Positive Correlation If the change in the two variables is in the same direction. E.g. Temperature and Sales of Ice-cream Negative Correlation If the change in the two variables is in the opposite direction. E.g. Temperature and Sales of Woollen clothes If there is a simultaneous changes in the variables due to direct or indirect cause-effect then there is a correlation between variables.
- 39. Correlation Coefficient Scatter Plot A scatterplot is a type of data display that shows the relationship between two numerical variables. Karl Pearson It measures the linear association between two numeric variables. Correlation coefficient is a statistical measure that indicates the extent to which two or more variables fluctuate in relation to each other. Spearman It measures the linear association between ranks assigned to individual items of two variables.
- 40. Regression If these functional relationship is linear in nature, it is called Linear Regression. The regression line is given as 𝑦 = a + 𝑏𝑦𝑥 𝑥 𝒃𝒚𝒙 is the regression coefficient, which measures the change in variable 𝑦 for a unit change in independent variable 𝑥 . Regression is the functional relationship between two or more variables, such that we can estimate value of dependent variable for given value of independent variable(s)
- 41. Time Series A time series is a time ordered sequence of observations taken at regular interval (e.g. Hourly, daily, weekly, monthly, quarterly, annually). Examples of Time Series • Daily: Stock Price, temperature Weekly: Retail sales of departmental store • Monthly: Unemployment rate, consumer price index • Quarterly: GDP of a country, Yearly: Production of crops
- 42. Multivariate Analysis Multivariate analysis is stated to be an analysis of any concurrent relation between more than two variables or attributes. Some of the measures in Multivariate Analysis: • Multiple Correlation • Multiple Regression • Discriminant Analysis • ANOVA • Structural Equation Modelling
- 43. References https://ncert.nic.in/textbook.php?kest1=7-9 Std_11 - Google Drive Std_12 - Google Drive https://cdn1.byjus.com/wp-content/uploads/2020/07/GSEB- Class-12-Statistics-Part-1-Textbook-Commerce-Stream.pdf https://schools.freshersnow.com/wp- content/uploads/2021/12/Std-12-Statistics-Part-2-E.M.pdf
- 44. THANK YOU Dr Parag Shah | M.Sc., M.Phil., Ph.D. ( Statistics) pbshah@hlcollege.edu www.paragstatistics.wordpress.com