PHARMACY PRACTICE-I (PHARM. MATHEMATICS AND BIOSTATISTICS)
(Theory)
Paper
6 Marks 100
PART A: (PHARMACEUTICAL
MATHEMATICS) (40
MARKS)
1. ALGEBRA:
(a)
Solution of
Linear and Quadratic Equations: Equations reducible to Quadratic Form.
Solution of simultaneous Equations.
(b)
Arithmetic,
Geometric and Harmonic Progressions: Arithmetic, Geometric and Harmonic
Means.
(c)
Permutations and
Combinations:
(d)
Binomial
Theorem: Simple application.
2. TRIGONOMETRY:
Measurement of Angles in Radian and Degrees. Definitions of circular functions.
Derivation of circular function for simple cases.
3. ANALYTICAL
GEOMETRY: Coordinates of point in a plane. Distance between two points
in a plane. Locus, Equations of straight line, Equation of Parabola, Circle and
Ellips.
4. DIFFERENTIAL
CALCULUS: Functions, variations in functions, limits, differential coefficient, differentiation of
algebraic, trigonometric, exponential and logarithmic functions, partial
derivatives. Maxima and minima values. Points of inflexion.
5. INTEGRAL
CALCULUS: Concept of integration, Rules of integration, Integration of
algebraic, exponential, logarithmic and trigonometric functions by using
different techniques and numerical integration.
PART B:
(BIOSTATISTICS) (60 MARKS)
1. DESCRIPTION OF STATISTICS: Descriptive
Statistics: What is Statistics? Importance of Statistics. What is
Biostatistics? Application of Statistics in Biological and Pharmaceutical
Sciences. How samples are selected?
2 ORGANIZING
and DISPLAYING DATA: Vriables, Quantitative and Qualitative Variables, Univariate Data,
Bivariate Data, Random Variables, Frequency Table, Diagrams, Pictograms, Simple
Bar Charts, Multiple Bar Charts, Histograms.
3. SUMMARIZING
DATA and VARIATION: The Mean, The Median, The Mode, The
Mean
Deviation, The Variance and Standard Deviation, Coefficient of Variation.
4. CURVE
FITTING: Fitting a Straight Line. Fitting of Parabolic or High Degree
Curve.
5. PROBABILITY:
Definitions, Probability Rules, Probability Distributions (Binomial &
Normal Distributions).
6. SIMPLE REGRESSION AND CORRELATION:
Introduction. Simple Linear
Regression Model. Correlation co-efficient.
7. TEST OF
HYPOTHESIS AND SIGNIFICANCE: Statistical Hypothesis. Level of
Significance. Test of Significance. Confidence Intervals, Test involving
Binomial and Normal Distributions.
8. STUDENT
“t”, “F” and Chi-Square Distributions: Test of Significance based on
―t‖, ―F‖ and Chi-Square distributions.
9. ANALYSIS OF
VARIANCE: One-way Classification, Two-way Classification,
Partitioning of Sum of Squares and Degrees of Freedom, Multiple
Compression Tests such as LSD, The analysis of Variance Models.
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