Inferential statistics
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The document discusses inferential statistics and its applications. It defines statistics as dealing with collecting, classifying, presenting, comparing, and interpreting numerical data to make inferences about a population. Inferential statistics help decision makers present information, draw conclusions from samples, seek relationships between variables, and make reliable forecasts. The document also distinguishes between descriptive statistics, parametric inferential statistics that assume normal distributions, and non-parametric inferential statistics that make no distribution assumptions.
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Univariate & bivariate analysissristi1992 UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
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Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
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4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
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Statistics 000000000000000000000000.pptxshahanieabbat3 STATISTICS FOR GRADE 7 IN MATHEMATICS. STATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSSTATISTICS FOR GRADE 7 IN MATHEMATICSS
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1) List the numbers: 3, 8, 6, 10
2) Find the mean (average): (3 + 8 + 6 + 10) / 4 = 7
3) Find the deviations from the mean:
Deviation of 3 = 3 - 7 = -4
Deviation of 8 = 8 - 7 = 1
Deviation of 6 = 6 - 7 = -1
Deviation of 10 = 10 - 7 = 3
4) Square the deviations: (-4)2 = 16, 12 = 1, (-1)2 = 1, 32 = 9
5) Sum the squared deviations: 16 + 1 + 1 + 9 =
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Unit 4.pptxSamruddhi Chepe Evaluation Unit 4
Statistics in the View point of Evaluation
Unit 4 Syllabus-
4.2.1- Measuring Scales- Meaning and Statistical Use
4.2.2- Conversion and interpretation of Test Score
4.2.3- Normal Probability Curve
4.2.4- Central Tendency and its importance in Evaluation.
4.2.5- Dimensions of Deviation
The Unit 4 is all about Statistics…
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data.
In other words, it is a mathematical discipline to collect, summarize data.
Also, we can say that statistics is a branch of applied mathematics.
Statistics is simply defined as the study and manipulation of data. As we have already discussed in the introduction that statistics deals with the analysis and computation of numerical data.
Projective methods of Evaluation through Statistics-
Measurement is a process of assigning numbers to individuals or their characteristics according to specific rules.” (Eble and Frisbie, 1991, p.25).
This is very common and simple definition of the term ‘measurement’.
You can say that measurement is a quantitative description of one’s performance. Gay (1991) further simplified the term as a process of quantifying the degree to which someone or something possessed a given trait, i.e., quality, characteristics, or features.
Measurement assigns a numeral to quantify certain aspects of human and non-human beings.
It is numerical description of objects, traits, attributes, characteristics or behaviours.
Measurement is not an end in itself but definitely a means to evaluate the abilities of a person in education and other fields as well.
Measurement Scale-
Whenever we measure anything, we assign a numerical value. This numerical value is known as scale of measurement. A scale is a system or scheme for assigning values or scores to the characteristics being measured (Sattler, 1992). Like for measuring any aspect of the human being we assign a numeral to quantify it, further we can provide an order to it if we know the similar type of measurement of other members of the group, we can also make groups considering equal interval scores within the group.
Psychologist Stanley Stevens developed the four common scales of measurement:
Nominal
Ordinal
Interval &
Ratio
Each scale of measurement has properties that determine how to properly analyze the data.
Nominal scale-
In nominal scale, a numeral or label is assigned for characterizing the attribute of the person or thing.
That caters no order to define the attribute as high-low, more-less, big-small, superior-inferior etc.
In nominal scale, assigning a numeral is purely an individual matter.
It is nothing to do with the group scores or group measurement.
Statistics such as frequencies, percentages, mode, and chi-square tests are used in nominal measurement.
Examples include gender (male, female), colors (red, blue, green), or types of fruit (apple, banana, orange).
Ordinal scale-
Ordinal scale is synonymous to ranking or g
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🎯 Created for beginners, yet insightful for marketers at all levels
📈 Includes real-world examples and platform-specific tips
💡 Breaks complex ideas into fun, understandable slides
🌍 Who Should Read This:
Aspiring digital marketers 👩💻
Entrepreneurs & small business owners 🧑🍳
Students in marketing or business 📚
Social media beginners looking to learn fast 🚀
Anyone wanting to grow a brand or idea online 💼
👀 Curious about how Facebook Live helped a small tiffin service explode in popularity? Or how Instagram Reels took a plant-based café viral overnight?
You’ll find those stories and many more inside!
📌 Don't just scroll. Learn how to soar.
Start your journey into Social Media Marketing with this insightful, step-by-step guide!
🔗 Feel free to share, embed, or download and begin crafting your own social media success story today.
Article Link:
https://estudy.in/digital-marketing/start-social-media-marketing-beginners-guide/
A Content Goldmine: Using Internal Data Sources for Digital PR
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Winning The Link Game: How To Create & Pitch Content That Attracts Incredible...
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Meanwhile, top brands are leveraging powerful linkable assets to earn high-quality links that truly impact search rankings. Your brand can too.
Watch us as we unveil Resolve Marketing’s proven approach to earning incredible links from authoritative domains using data-driven linkable assets.
Here’s what you’ll learn:
-Link Diversity & Relevance Are Key – Learn why a strategic mix of high-quality links matters more than ever.
-Digital PR Will Help You Move the Needle – Discover how digital PR can secure impactful backlinks.
-The Keys to Building Your Own Digital PR Campaign – Walk away with a step-by-step framework to implement immediately.
With Michael Johnson from Resolve, we’ll dive into the state of link building in 2025, exploring evolving trends and Google’s latest ranking factors. You’ll also hear the key elements of great content that’s converting this year, with the strategies that worked for Resolve.
Storytelling Master Class - Andrew Jenkins, Volterra
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Move your audience, build trust, and inspire action, one story at a time. In today’s content-saturated world, storytelling separates forgettable posts from powerful ones. Whether in B2B, B2C, or running your own business, this session will show you how to create content that resonates and drives results. You’ll learn how to:
• Craft brand and customer stories that connect emotionally
• Identify when storytelling strengthens or weakens your message
• Use authentic voices and experiences to build authenticity
• Apply storytelling to blogs, social, campaigns, and more
We’ll look at case studies, strong (and weak) examples from authentic brands, and practical takeaways you can apply immediately. If your content feels like it’s missing something or isn’t performing as it should, this session will help you make it more impactful, memorable, and worth your audience’s time.
Key Takeaways:
• Understand the core elements of compelling storytelling and how they apply to digital content.
• Learn how to identify and structure brand and customer stories that drive engagement.
• Discover the difference between content that tells an engaging story and content that just delivers information.
• Gain insights from real-world case studies, including what works, what flops, and why.
• Learn how to use storytelling across formats: blogs, social media, campaigns, and video.
• Walk away with practical techniques to humanize your brand and make your content more relatable.
• Recognize common storytelling pitfalls and how to avoid them.
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Inferential statistics
- 1. Inferential Statistics Applications & Usefulness
- 2. Definitions • Statistics is the science which deals with the methods of collecting, classifying, presenting, comparing and interpreting numerical data collected to have some inference regarding the real aspects. Highlights: (i) Aggregates of facts. (ii) Effected to a marked extent by multiplicity of causes. (iii) Numerically Expressed. (iv) Estimated as per reasonable standards of accuracy. (v) Collected in as systematic manner for a pre- determined purpose.
- 3. Reasons for Learning Statistics It enables the decision-maker to understand how to : (i) present and describe information (data) so as to improve decisions. (ii) draw conclusions about the large population based upon information obtained from samples. (iii) seek out relationship between pair of variables to improve processes. (iv) obtain reliable forecasts of statistical variables of interest.
- 4. Statistical Thinking and Analysis Statistical thinking can be defined as the thought process that focuses on ways to identify, control and reduce variations present in all phenomena. Management Philosophy Statistical Methods Behavioral Tools Quality Improvement Process Model Management Philosophy acts as a guide for laying a solid foundation for total quality improvement efforts. However, Behavioral Tools such as brainstorming, team-building & Group Decision making, & Statistical Methods like tables, control-charts and descriptive statistics are proved to be as pillars for quality improvement.
- 5. Types of Statistical Methods • Descriptive statistics - Graphical Presentations like Bar charts, line , pie charts, frequency distribution. – Measures of Central Tendency ( Mean , Median , Mode). – Measures of Dispersion ( Range, Quartile, Mean Deviation, Standard Deviation). • Inferential statistics - Statistical Methods to estimate the characteristics of the population based on sample results.
- 6. Inferential Statistics • Can be categorized as: - Parametric - Non- Parametric Parametric - Assumed that the drawn sample is normally distributed. - Data collection takes place on an interval or ratio scale. Non-Parametric - Makes no explicit assumption regarding the normality. - Data collection takes place on an nominal or ordinal scale.
- 7. Need of Data • To provide necessary inputs to a given phenomenon or situation under study. • To measure performance in an ongoing process such as production, service and so on. • To enhance the quality of decision-making by enumerating various options. • To satisfy the desire to understand an unknown phenomenon. • To assist in guessing the causes and probable effects of certain characteristics in given situations.
- 8. Types of Data • Categorical – Do you practice Yoga? Yes No • Numerical - Discrete - How many books are there in library? Exact Number - No of Students in the class? (Integer) - Continuous - What is your height? Centimeters or Inches - What is the temperature? Degree or Fahrenheit
- 9. Types of Scales • Nominal Scale • Ordinal Scale • Interval Scale • Ratio Scale
- 10. Nominal Scale • A scale of measurement for a variable that uses a label (or name) to identify an attribute of an element of the data set. e.g. Each of the respondent has to fit into following category: - Indian - Sri Lankan - Nepalese - Bhootanis - Pakistanis - Others Other Examples: Grouping of Individuals or objects. Your gender : Male Female Your place of work : Production Finance Sales
- 11. Ordinal Scale • A scale of measurement for a variable that is used to rank (or order) observations in the data set. e.g. Ranking of the items, say from 10 to 1, depending of their usage. Specifically, if particular system is not used at all in your office, put a less figure against it. IBM/AT Compaq IBM/XT AT&T Apple II Tandy 2000 Macintosh Zenith HCL
- 12. Interval Scale • A scale of measurement for a variable in which the interval between observations is expressed in terms of a fixed standard unit of measurement. e.g. when we measure temperature, the distance from 30-40 is same as distance from 70-80.
- 13. Ratio Scale • A scale of measurement for a variable that has interval which is measurable in standard unit of measurement and a meaningful zero, i.e. the ratio of two values is meaningful.