Topic outline

  • الاحصاء الاستدلالي

    الاحصاء الاستدلالي

    المستوى: سنة ثانية ليسانس

    شعبة: التدريب الرياضي + النشاط البدني الرياضي التربوي

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 

    • مدخل الى الاحصاء

      تهدف هذه المحاضرة الى وضع الطالب في الصورة من حيث بعض المصطلحات الاحصائية وفهمها ليتسنى له التعامل مع القوانيين الاحصائية التي سيتطرق لها من بعد

    • اختبار ستيوذنت ( T- test )


       في هذا الموضوع سوف نلقى الضوء على أهم اختبار احصائي والأشهر والأكثر استخداما وبخاصة في البحوث التربوية والرياضية وهو اختبار ستيوذنت ( T- test )  

           هو أحد أهم الاختبارات الإحصائية وأكثرها استخداما في الأبحاث والدراسات التي تهدف للكشف عن دلالة الفروق الإحصائية بين متوسطي عينتين.

    • اختبار كا2

      يتيح اختبار الاستقلالية بين متغيرين، أو ما يصطلح عليه اختبار كا2 أو اختبار كاى مربع، إمكانية الجواب على سؤال: هل المعطيات المستخرجة من معاينة عشوائية تمكن من استنتاج الاستقلالية بين متغيرين كيفيين من نفس العينة؟

    • معامل الارتباط

      من اساليب التحليل الاحصائي ما يسمى بالارتباط وهو مفهوم احصائي يوضح العلاقة بين متغيريين او اكثر ، وفحصها ما اذا كانت ايجابية او سلبية وكذا نوعها عكسية ام طردية وايضا مدى قوتها