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Showing posts from March 5, 2026

Chapter-8 Use of Statistical Tools

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📘 Chapter 8 Important Topics: Use of Statistical Tools (à´¸്à´¥ിà´¤ിà´µിവര ഉപകരണങ്ങളുà´Ÿെ ഉപയോà´—ം), Steps in Making a Project (à´’à´°ു à´ª്à´°ോജക്à´Ÿ് തയ്à´¯ാà´±ാà´•്à´•ുà´¨്നതിà´¨ുà´³്à´³ ഘട്à´Ÿà´™്ങൾ), Identifying a Problem (à´ª്à´°à´¶്à´¨ം à´¤ിà´°ിà´š്à´šà´±ിയൽ), Choice of Target Group (ലക്à´·്à´¯ à´—്à´°ൂà´ª്à´ªിà´¨്à´±െ à´¤ിà´°à´ž്à´žെà´Ÿുà´ª്à´ª്), Collection of Data (ദത്à´¤ à´¶േà´–à´°à´£ം), Organisation and Presentation (à´•്à´°à´®ീകരണവും അവതരണവും), Analysis and Interpretation (à´µിശകലനവും à´µ്à´¯ാà´–്à´¯ാനവും), Conclusion and Bibliography, Sample Project (Toothpaste Market Survey), Glossary of Statistical Terms A project involves identifying a problem, collecting data, and analyzing it using statistical tools. Target group selection is crucial for relevant data collection. Data can be primary (collected by you) or secondary (from existing sources). Organize data using tables, bar diagrams, pie charts, histograms, etc. Analyze using mean, median, mode, standard deviation, correlation. Draw conclusions and suggest improvements based on findings. A sa...
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Chapter-7 Index Numbers

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📘 Chapter 7 Important Topics: Index Numbers (à´¸ൂà´šà´• à´¸ംà´–്യകൾ), Meaning and Definition (അർത്ഥവും à´¨ിർവചനവും), Construction Methods (à´¨ിർമ്à´®ാà´£ à´°ീà´¤ികൾ) – Simple Aggregative, Weighted Aggregative (Laspeyre's, Paasche's), Averaging Relatives, Important Index Numbers (CPI, WPI, IIP, HDI, SENSEX), Issues in Construction, Uses in Economics Index number measures average change in a group of related variables over time. Base period is the reference period with index 100. Simple aggregative index: (ΣP₁/ΣP₀) × 100 (ignores weights). Weighted aggregative indices: Laspeyre's (base year weights), Paasche's (current year weights). CPI measures retail price changes; WPI measures wholesale price changes; IIP measures industrial production. SENSEX is the stock market index of BSE; HDI measures human development. Chapter-7 Index Numbers (à´¸ൂà´šà´• à´¸ംà´–്യകൾ) Introduction (ആമുà´–ം) You have learnt in the previous chapters how summary measures can be obtai...
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Chapter-6 Correlation

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📘 Chapter 6 Important Topics: Correlation (സഹബന്à´§ം), Types of Correlation (Positive, Negative), Scatter Diagram (à´šിതറിà´¯ à´°േà´–ാà´šിà´¤്à´°ം), Karl Pearson's Coefficient of Correlation (à´•ാൾ à´ªിà´¯േà´´്സന്à´±െ സഹബന്à´§ à´—ുണകം), Properties of Correlation Coefficient, Spearman's Rank Correlation (à´¸്à´ªിയർമാà´¨്à´±െ à´±ാà´™്à´•് സഹബന്à´§ം) – Ranks given, Ranks not given, Repeated ranks Correlation measures the direction and intensity of relationship between two variables. Positive correlation: variables move in the same direction. Negative correlation: variables move in opposite directions. Scatter diagram gives a visual idea of the relationship. Karl Pearson's r measures linear correlation; range -1 to +1. Spearman's rank correlation is used when data is in ranks or qualitative. Chapter-6 Correlation (സഹബന്à´§ം) Introduction (ആമുà´–ം) In previous chapters you have learnt how to construct summary measures out of a mass of data and changes among similar variables...
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Chapter-5 Measures of Central Tendency

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```html 📘 Chapter 5 Important Topics: Measures of Central Tendency (à´•േà´¨്à´¦്à´° à´ª്രവണതയുà´Ÿെ അളവുകൾ), Arithmetic Mean (സമാà´¨്തര à´®ാà´§്à´¯ം) – Direct, Assumed Mean, Step Deviation Methods, Weighted Mean, Median (മധ്യമം) – Individual, Discrete, Continuous Series, Quartiles (à´šà´¤ുർത്ഥകങ്ങൾ), Mode (ബഹുലകം) – Individual, Discrete, Continuous Series, Relative Position of Mean, Median & Mode Central tendency gives a single representative value for a data set. Arithmetic mean is the sum of all observations divided by number of observations. Mean is affected by extreme values; median and mode are not. Median is the middle value when data is arranged in order. Mode is the most frequently occurring value. Quartiles divide data into four equal parts (Q1, Q2, Q3). Chapter-5 Measures of Central Tendency (à´•േà´¨്à´¦്à´° à´ª്രവണതയുà´Ÿെ അളവുകൾ) Introduction (ആമുà´–ം) In the previous chapter, you have read about the tabular and graphic representation of the data. In this chapt...
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Chapter-4 Presentation of Data

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📘 Chapter 4 Important Topics: Presentation of Data (ദത്തങ്ങളുà´Ÿെ അവതരണം), Textual Presentation (à´µാà´šിà´• അവതരണം), Tabular Presentation (പട്à´Ÿിà´• അവതരണം) – Parts of a Table (പട്à´Ÿിà´•à´¯ുà´Ÿെ à´­ാà´—à´™്ങൾ), Diagrammatic Presentation (à´šിà´¤്à´°à´°ൂà´ª അവതരണം) – Bar Diagrams (Simple, Multiple, Component), Pie Diagram (à´µൃà´¤്à´¤ à´šിà´¤്à´°ം), Frequency Diagrams (Histogram, Frequency Polygon, Frequency Curve, Ogive), Arithmetic Line Graph (Time Series Graph) Data presentation has three forms: textual, tabular, and diagrammatic. Tabular presentation organizes data in rows and columns with clear headings. Parts of a table: Table number, Title, Captions, Stubs, Body, Unit, Source, Note. Bar diagrams are one-dimensional; pie diagrams show components as parts of a circle. Histogram, frequency polygon, and ogive are used for grouped frequency data. Arithmetic line graph shows trends over time. Chapter-4 Presentation of Data (ദത്തങ്ങളുà´Ÿെ അവതരണം) Introduction (ആമുà´–ം) You have already ...
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Chapter-3 Organisation of Data

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📘 Chapter 3 Important Topics: Introduction to Classification (വർഗ്à´—ീകരണത്à´¤ിà´¨് à´’à´°ു ആമുà´–ം), Raw Data (à´…à´¸ംà´¸്à´•ൃà´¤ ദത്തങ്ങൾ), Types of Classification (വർഗ്à´—ീകരണത്à´¤ിà´¨്à´±െ തരങ്ങൾ: Chronological, Spatial, Qualitative, Quantitative), Continuous and Discrete Variables (à´¤ുടർച്à´šà´¯ായതും à´µിà´š്à´›ിà´¨്നവുà´®ാà´¯ à´šà´°à´™്ങൾ), Frequency Distribution (ആവൃà´¤്à´¤ി à´µിതരണം), Class Limits, Class Interval, Class Mark (à´•്à´²ാà´¸് പരിà´§ികൾ, à´•്à´²ാà´¸് à´…à´¨്തരം, à´•്à´²ാà´¸് മധ്യബിà´¨്à´¦ു), How to prepare Frequency Distribution (ആവൃà´¤്à´¤ി à´µിതരണം à´Žà´™്ങനെ തയ്à´¯ാà´±ാà´•്à´•ാം), Inclusive and Exclusive Methods (ഇൻക്à´²ൂà´¸ീà´µ്, à´Žà´•്à´¸്à´•്à´²ൂà´¸ീà´µ് à´°ീà´¤ികൾ), Tally Marking (à´Ÿാà´²ി à´®ാർക്à´•ിംà´—്), Loss of Information (à´µിവരങ്ങളുà´Ÿെ നഷ്à´Ÿം), Bivariate Frequency Distribution (à´¦്à´µിà´šà´° ആവൃà´¤്à´¤ി à´µിതരണം) Raw data is unorganised and difficult to interpret. Classification groups data based on similarities (time, place, quality, quantity). Continuous variables can take any value; discrete variables take only certain values. Frequency distribution shows how data is distri...
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