{"id":1423,"date":"2025-06-29T12:07:32","date_gmt":"2025-06-29T12:07:32","guid":{"rendered":"https:\/\/itxperts.co.in\/blog\/?p=1423"},"modified":"2025-08-02T09:29:30","modified_gmt":"2025-08-02T09:29:30","slug":"cbse-class-10-ai-unit-4-statistical-data","status":"publish","type":"post","link":"https:\/\/itxperts.co.in\/blog\/cbse-class-10-ai-unit-4-statistical-data\/","title":{"rendered":"Statistical Data"},"content":{"rendered":"\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>A. Introduction to Statistical Data<\/strong><\/h2>\n\n\n\n<p>Statistics is a branch of mathematics that helps us collect, organize, analyze, and interpret data.<\/p>\n\n\n\n<p>In Artificial Intelligence, <strong>statistical data<\/strong> is used to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Understand data patterns<\/li>\n\n\n\n<li>Make predictions<\/li>\n\n\n\n<li>Train and test models<\/li>\n<\/ul>\n\n\n\n<p>Before building any AI model, we need to <strong>study the data using basic statistics<\/strong>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>B. Types of Data<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Qualitative Data (Categorical)<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Data that describes qualities or categories.<\/li>\n\n\n\n<li>Cannot be measured numerically.<\/li>\n\n\n\n<li>Examples: Gender, color, type of food, etc.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Quantitative Data (Numerical)<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Data that can be measured or counted.<\/li>\n\n\n\n<li>Can be divided into:\n<ul class=\"wp-block-list\">\n<li><strong>Discrete Data<\/strong>: Countable (e.g., number of students)<\/li>\n\n\n\n<li><strong>Continuous Data<\/strong>: Measurable (e.g., height, weight, temperature)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>C. Measures of Central Tendency<\/strong><\/h2>\n\n\n\n<p>These are values that represent the center or average of a dataset.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Mean (Average)<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Sum of all data values divided by the number of values.<\/li>\n<\/ul>\n\n\n\n<p><strong>Formula<\/strong>:<br>Mean=Sum&nbsp;of&nbsp;all&nbsp;valuesNumber&nbsp;of&nbsp;values\\text{Mean} = \\frac{\\text{Sum of all values}}{\\text{Number of values}}Mean=Number&nbsp;of&nbsp;valuesSum&nbsp;of&nbsp;all&nbsp;values\u200b<\/p>\n\n\n\n<p><strong>Example<\/strong>:<br>Marks = 40, 50, 60<br>Mean = (40 + 50 + 60) \/ 3 = 150 \/ 3 = <strong>50<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Median<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The middle value when data is arranged in order.<\/li>\n\n\n\n<li>If even number of values, median is the average of the two middle values.<\/li>\n<\/ul>\n\n\n\n<p><strong>Example (Odd)<\/strong>:<br>Marks = 20, 30, 40 \u2192 Median = <strong>30<\/strong><\/p>\n\n\n\n<p><strong>Example (Even)<\/strong>:<br>Marks = 20, 30, 40, 50 \u2192 Median = (30 + 40)\/2 = <strong>35<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3. Mode<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The value that appears most frequently.<\/li>\n<\/ul>\n\n\n\n<p><strong>Example<\/strong>:<br>Marks = 20, 30, 30, 40 \u2192 Mode = <strong>30<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>D. Measures of Dispersion (Spread of Data)<\/strong><\/h2>\n\n\n\n<p>These measures tell us how spread out or varied the data is.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Range<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The difference between the maximum and minimum values.<\/li>\n<\/ul>\n\n\n\n<p><strong>Formula<\/strong>:<br>Range=Maximum&nbsp;value\u2212Minimum&nbsp;value\\text{Range} = \\text{Maximum value} &#8211; \\text{Minimum value}Range=Maximum&nbsp;value\u2212Minimum&nbsp;value<\/p>\n\n\n\n<p><strong>Example<\/strong>:<br>Marks = 20, 30, 40<br>Range = 40 \u2013 20 = <strong>20<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Standard Deviation<\/strong> <em>(for higher understanding, optional for basics)<\/em><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Shows how much the values differ from the mean.<\/li>\n\n\n\n<li>A low standard deviation means the data is close to the mean.<\/li>\n\n\n\n<li>A high standard deviation means the data is spread out.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>E. Data Visualization Techniques<\/strong><\/h2>\n\n\n\n<p>Data can be better understood using charts and graphs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Bar Graph<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Used to represent <strong>categorical data<\/strong>.<\/li>\n\n\n\n<li>Each bar shows the frequency of a category.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Histogram<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Used to represent <strong>numerical data<\/strong> (like marks or height).<\/li>\n\n\n\n<li>Shows how data is distributed over intervals.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3. Pie Chart<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Circular chart divided into slices.<\/li>\n\n\n\n<li>Each slice shows the proportion of a category.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4. Line Graph<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Shows trends over time (e.g., temperature by days).<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>F. Using Python Libraries to Analyze Data (Overview Only)<\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>NumPy<\/strong> and <strong>Pandas<\/strong> libraries in Python help us perform statistical calculations.<\/li>\n\n\n\n<li><strong>Matplotlib<\/strong> and <strong>Seaborn<\/strong> are used for drawing graphs.<\/li>\n<\/ul>\n\n\n\n<p><strong>Example using Python (For practical understanding):<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">pythonCopyEdit<code>import numpy as np\n\nmarks = [40, 50, 60]\nmean = np.mean(marks)\nmedian = np.median(marks)\nprint(\"Mean:\", mean)\nprint(\"Median:\", median)\n<\/code><\/pre>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>G. Real-Life Examples<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Scenario<\/th><th>Statistical Method Used<\/th><\/tr><\/thead><tbody><tr><td>Checking average marks in a class<\/td><td>Mean<\/td><\/tr><tr><td>Finding the most common favorite fruit<\/td><td>Mode<\/td><\/tr><tr><td>Analyzing the difference in heights<\/td><td>Range \/ Standard Deviation<\/td><\/tr><tr><td>Showing number of boys vs. girls<\/td><td>Bar Graph<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>H. Activity Suggestion for Students<\/strong><\/h2>\n\n\n\n<p>Ask students to collect data of their classmates such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Marks in a test<\/li>\n\n\n\n<li>Height in cm<\/li>\n\n\n\n<li>Favorite color<\/li>\n<\/ul>\n\n\n\n<p>Then:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Calculate Mean, Median, and Mode<\/li>\n\n\n\n<li>Draw a bar graph or pie chart<\/li>\n<\/ul>\n\n\n\n<p>Discuss:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Which measure gives the best idea of the data?<\/li>\n\n\n\n<li>Are there any outliers (values that are too high or low)?<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>I. Keywords to Remember<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Term<\/th><th>Meaning<\/th><\/tr><\/thead><tbody><tr><td>Mean<\/td><td>The average value of data<\/td><\/tr><tr><td>Median<\/td><td>The middle value in ordered data<\/td><\/tr><tr><td>Mode<\/td><td>The most frequently occurring value<\/td><\/tr><tr><td>Range<\/td><td>The difference between the highest and lowest values<\/td><\/tr><tr><td>Qualitative Data<\/td><td>Data in categories or labels<\/td><\/tr><tr><td>Quantitative Data<\/td><td>Data in numbers or measurable quantities<\/td><\/tr><tr><td>Bar Graph<\/td><td>Chart to represent categories<\/td><\/tr><tr><td>Histogram<\/td><td>Chart to show frequency distribution<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>J. Summary of the Unit<\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Statistics helps us understand data through <strong>averages<\/strong> and <strong>visualizations<\/strong>.<\/li>\n\n\n\n<li>We learned how to calculate <strong>Mean, Median, Mode, and Range<\/strong>.<\/li>\n\n\n\n<li>We understood how to use <strong>charts and graphs<\/strong> to visualize data.<\/li>\n\n\n\n<li>These tools are essential for exploring data before building an AI model.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>A. Introduction to Statistical Data Statistics is a branch of mathematics that helps us collect, organize, analyze, and interpret data. In Artificial Intelligence, statistical data is used to: Before building any AI model, we need to study the data using basic statistics. B. Types of Data 1. Qualitative Data (Categorical) 2. Quantitative Data (Numerical) C. [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"googlesitekit_rrm_CAow44u0DA:productID":"","footnotes":""},"categories":[212],"tags":[201],"class_list":["post-1423","post","type-post","status-publish","format-standard","hentry","category-subject-specific-skills-class-10th","tag-aiclass-x"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Statistical Data - Itxperts<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/itxperts.co.in\/blog\/cbse-class-10-ai-unit-4-statistical-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Statistical Data - Itxperts\" \/>\n<meta property=\"og:description\" content=\"A. 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In Artificial Intelligence, statistical data is used to: Before building any AI model, we need to study the data using basic statistics. B. Types of Data 1. Qualitative Data (Categorical) 2. Quantitative Data (Numerical) C. 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