In this following article we share PDF link of CLINICAL MEDICINE LECTURE NOTES 8TH Edition free with a quick review and features. You can easily download the PDF from the end section of this post by clicking the link.

Clinical Medicine Lecture Notes is a concise guide with an updated content throughout the text. Featuring guide lecture notes for history taking and examination as well as essentials of clinical medicine on a system-by-system basis. Two section separation of this notes, part one exploring communication and physical examination techniques while the second part of the text covers a range of common diseases. A comprehensive guide to provide core knowledge required for assessing and diagnosing diseases in the main systems of the body.

Features Of Lectures Notes Clinical Medicine 8th Edition PDF:

Following are the few important feature are given below;

• High-yield summary and evidence-based medicine boxes covering both the clinical approach and the essential background knowledge
• OSCE exam summaries that medical students and junior doctors need to know.
•  Full-colour illustrations and photographs with updated content throughout the text.

TABLE OF CONTENTS:

Following are the complete contents of Clinical Medicine Lecture Notes;

1 Data and Case Studies 1

• 1.1 Case Study: Flight Delays 1
• 1.2 Case Study: BirthWeights of Babies 2
• 1.3 Case Study: Verizon Repair Times 3
• 1.4 Case Study: Iowa Recidivism 4
• 1.5 Sampling 5
• 1.6 Parameters and Statistics 6
• 1.7 Case Study: General Social Survey 7
• 1.8 Sample Surveys 8
• 1.9 Case Study: Beer and HotWings 9
• 1.10 Case Study: Black Spruce Seedlings 10
• 1.11 Studies 10
• 1.12 Google Interview Question: Mobile Ads Optimization 12
• Exercises 16

2 Exploratory Data Analysis 21

• 2.1 Basic Plots 21
• 2.2 Numeric Summaries 25
• 2.2.1 Center 25
• 2.2.2 Spread 26
• 2.2.3 Shape 27
• 2.3 Boxplots 28
• 2.4 Quantiles and Normal Quantile Plots 29
• 2.5 Empirical Cumulative Distribution Functions 35
• 2.6 Scatter Plots 38
• 2.7 Skewness and Kurtosis 40

3 Introduction to Hypothesis Testing: Permutation Tests 47

• 3.1 Introduction to Hypothesis Testing 47
• 3.2 Hypotheses 48
• 3.3 Permutation Tests 50
• 3.3.1 Implementation Issues 55
• 3.3.2 One-sided and Two-sided Tests 61
• 3.3.3 Other Statistics 62
• 3.3.4 Assumptions 64
• 3.3.5 Remark on Terminology 68
• 3.4 Matched Pairs 68
• Exercises 70

4 Sampling Distributions 75

• 4.1 Sampling Distributions 75
• 4.2 Calculating Sampling Distributions 80
• 4.3 The Central LimitTheorem 84
• 4.3.1 CLT for Binomial Data 86
• 4.3.2 Continuity Correction for Discrete Random Variables 89
• 4.3.3 Accuracy of the Central Limit Theorem∗ 91
• 4.3.4 CLT for SamplingWithout Replacement 92
• Exercises 93

5 Introduction to Confidence Intervals: The Bootstrap 103

• 5.1 Introduction to the Bootstrap 103
• 5.2 The Plug-in Principle 110
• 5.2.1 Estimating the Population Distribution 112
• 5.2.2 How Useful Is the Bootstrap Distribution? 113
• 5.3 Bootstrap Percentile Intervals 118
• 5.4 Two-Sample Bootstrap 119
• 5.4.1 Matched Pairs 124
• 5.5 Other Statistics 128
• 5.6 Bias 131
• 5.7 Monte Carlo Sampling: The “Second Bootstrap Principle” 134
• 5.8 Accuracy of Bootstrap Distributions 135
• 5.8.1 Sample Mean: Large Sample Size 135
• 5.8.2 Sample Mean: Small Sample Size 137
• 5.8.3 Sample Median 138
• 5.8.4 Mean–Variance Relationship 138
• 5.9 HowMany Bootstrap Samples Are Needed? 140
• Exercises 141

6 Estimation 149

• 6.1 Maximum Likelihood Estimation 149
• 6.1.1 Maximum Likelihood for Discrete Distributions 150
• 6.1.2 Maximum Likelihood for Continuous Distributions 153
• 6.1.3 Maximum Likelihood for Multiple Parameters 157
• 6.2 Method of Moments 161
• 6.3 Properties of Estimators 163
• 6.3.1 Unbiasedness 164
• 6.3.2 Efficiency 167
• 6.3.3 Mean Square Error 171
• 6.3.4 Consistency 173
• 6.3.5 Transformation Invariance∗ 175
• 6.3.6 Asymptotic Normality of MLE∗ 177
• 6.4 Statistical Practice 178
• 6.4.1 Are You Asking the Right Question? 179
• 6.4.2 Weights 179
• Exercises 180
• 7 More Confidence Intervals 187
• 7.1 Confidence Intervals for Means 187
• 7.1.1 Confidence Intervals for a Mean, Variance Known 187
• 7.1.2 Confidence Intervals for a Mean, Variance Unknown 192
• 7.1.3 Confidence Intervals for a Difference in Means 198
• 7.1.4 Matched Pairs, Revisited 204
• 7.2 Confidence Intervals in General 204
• 7.2.1 Location and Scale Parameters∗ 208
• 7.3 One-sided Confidence Intervals 212
• 7.4 Confidence Intervals for Proportions 214
• 7.4.1 Agresti–Coull Intervals for a Proportion 217
• 7.4.2 Confidence Intervals for a Difference of Proportions 218
• 7.5 Bootstrap Confidence Intervals 219
• 7.5.1 t Confidence Intervals Using Bootstrap Standard Errors 219
• 7.5.2 Bootstrap t Confidence Intervals 220
• 7.5.3 Comparing Bootstrap t and Formula t Confidence Intervals 224
• 7.6 Confidence Interval Properties 226
• 7.6.1 Confidence Interval Accuracy 226
• 7.6.2 Confidence Interval Length 227
• 7.6.3 Transformation Invariance 227
• 7.6.4 Ease of Use and Interpretation 227
• 7.6.5 Research Needed 228
• Exercises 228

8 More Hypothesis Testing 241

• 8.1 Hypothesis Tests for Means and Proportions: One Population 241
• 8.1.1 A Single Mean 241
• 8.1.2 One Proportion 244
• 8.2 Bootstrap t-Tests 246
• 8.3 Hypothesis Tests for Means and Proportions: Two Populations 248
• 8.3.1 Comparing Two Means 248
• 8.3.2 Comparing Two Proportions 251
• 8.3.3 Matched Pairs for Proportions 254
• 8.4 Type I and Type II Errors 255
• 8.4.1 Type I Errors 257
• 8.4.2 Type II Errors and Power 261
• 8.4.3 P-Values versus Critical Regions 266
• 8.5 Interpreting Test Results 267
• 8.5.1 P-Values 267
• 8.5.2 On Significance 268
• 8.5.3 Adjustments for Multiple Testing 269
• 8.6 Likelihood Ratio Tests 271
• 8.6.1 Simple Hypotheses and the Neyman–Pearson Lemma 271
• 8.6.2 Likelihood Ratio Tests for Composite Hypotheses 275
• 8.7 Statistical Practice 279
• 8.7.1 More Campaigns with No Clicks and No Conversions 284
• Exercises 285

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9 Regression 297

• 9.1 Covariance 297
• 9.2 Correlation 301
• 9.3 Least-Squares Regression 304
• 9.3.1 Regression Toward the Mean 308
• 9.3.2 Variation 310
• 9.3.3 Diagnostics 311
• 9.3.4 Multiple Regression 317
• 9.4 The Simple LinearModel 317
• 9.4.1 Inference for 𝛼 and 𝛽 322
• 9.4.2 Inference for the Response 326
• 9.4.3 Comments about Assumptions for the Linear Model 330
• 9.5 Resampling Correlation and Regression 332
• 9.5.1 Permutation Tests 335
• 9.5.2 Bootstrap Case Study: Bushmeat 336
• 9.6 Logistic Regression 340
• 9.6.1 Inference for Logistic Regression 346
• Exercises 350

10 Categorical Data 359

• 10.1 Independence in Contingency Tables 359
• 10.2 Permutation Test of Independence 361
• 10.3 Chi-square Test of Independence 365
• 10.3.1 Model for Chi-square Test of Independence 366
• 10.3.2 2 × 2 Tables 368
• 10.3.3 Fisher’s Exact Test 370
• 10.3.4 Conditioning 371
• 10.4 Chi-square Test of Homogeneity 372
• 10.5 Goodness-of-fit Tests 374
• 10.5.1 All Parameters Known 374
• 10.5.2 Some Parameters Estimated 377
• 10.6 Chi-square and the Likelihood Ratio∗ 379
• Exercises 380

11 Bayesian Methods 391

• 11.1 Bayes Theorem 392
• 11.2 Binomial Data: Discrete Prior Distributions 392
• 11.3 Binomial Data: Continuous Prior Distributions 400
• 11.4 Continuous Data 406
• 11.5 Sequential Data 409
• Exercises 414

10 Glaucoma, 102

12 One-way ANOVA 419

• 12.1 Comparing Three or More Populations 419
• 12.1.1 The ANOVA F-test 419
• 12.1.2 A Permutation Test Approach 428
• Exercises 429

13 Additional Topics 433

• 13.1 Smoothed Bootstrap 433
• 13.1.1 Kernel Density Estimate 435
• 13.2 Parametric Bootstrap 437
• 13.3 The Delta Method 441
• 13.4 Stratified Sampling 445
• 13.5 Computational Issues in Bayesian Analysis 446
• 13.6 Monte Carlo Integration 448
• 13.7 Importance Sampling 452
• 13.7.1 Ratio Estimate for Importance Sampling 458
• 13.7.2 Importance Sampling in Bayesian Applications 461
• 13.8 The EM Algorithm 467
• 13.8.1 General Background 469
• Exercises 472

Appendix A Review of Probability 477

• A.1 Basic Probability 477
• A.2 Mean and Variance 478
• A.3 The Normal Distribution 480
• A.4 The Mean of a Sample of RandomVariables 481
• A.5 Sums of Normal Random Variables 482
• A.6 The Law of Averages 483
• A.7 Higher Moments and the Moment-generating Function 484

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